Home | [1–10] << 11 12 13 >> |
Records | Links | |||||
---|---|---|---|---|---|---|
Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta |
|
||||
Title | Bag of Negatives for Siamese Architectures | Type | Conference Article | |||
Year | 2019 | Publication | 30th British Machine Vision Conference | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy. | |||||
Address | Cardiff; United Kingdom; September 2019 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | BMVC | |||
Notes | CIC; 600.140; 600.118 | Approved | no | |||
Call Number | Admin @ si @ GAB2019b | Serial | 3263 | |||
Permanent link to this record | ||||||
Author | Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
|
||||
Title | Temporal Coherence Analysis for Intelligent Headlight Control | Type | Miscellaneous | |||
Year | 2008 | Publication | 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles | Abbreviated Journal | ||
Volume | Issue | Pages | 59–64 | |||
Keywords | Intelligent Headlights | |||||
Abstract | ||||||
Address | ||||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | IROS | |||
Notes | ADAS;CIC | Approved | no | |||
Call Number | ADAS @ adas @ LHB2008b | Serial | 1112 | |||
Permanent link to this record | ||||||
Author | Javier Vazquez; Robert Benavente; Maria Vanrell |
|
||||
Title | Naming constraints constancy | Type | Conference Article | |||
Year | 2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | Different studies have shown that languages from industrialized cultures
share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018]. |
|||||
Address | ||||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | AV A | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ VBV2012 | Serial | 2131 | |||
Permanent link to this record | ||||||
Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco |
|
||||
Title | An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction | Type | Conference Article | |||
Year | 2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here. |
|||||
Address | ||||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | AV A | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ OPD2012a | Serial | 2132 | |||
Permanent link to this record | ||||||
Author | Xavier Otazu; Maria Vanrell |
|
||||
Title | Several lightness induction effects with a computational multiresolution wavelet framework | Type | Journal | |||
Year | 2006 | Publication | 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | ||||||
Address | Saint-Petersburg (Russia) | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | ||||
Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ OtV2006 | Serial | 659 | |||
Permanent link to this record | ||||||
Author | Graham D. Finlayson; Javier Vazquez; Fufu Fang |
|
||||
Title | The Discrete Cosine Maximum Ignorance Assumption | Type | Conference Article | |||
Year | 2021 | Publication | 29th Color and Imaging Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 13-18 | |||
Keywords | ||||||
Abstract | the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between wavelengths (many of the assumed reflectances are, in reality, not possible). In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely. Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets. Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set. |
|||||
Address | Virtual; November 2021 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | CIC | |||
Notes | CIC | Approved | no | |||
Call Number | FVF2021 | Serial | 3596 | |||
Permanent link to this record | ||||||
Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
|
||||
Title | The Photometry of Intrinsic Images | Type | Conference Article | |||
Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 1494-1501 | |||
Keywords | ||||||
Abstract | Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. | |||||
Address | Columbus; Ohio; USA; June 2014 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | CVPR | |||
Notes | CIC; 600.052; 600.051; 600.074 | Approved | no | |||
Call Number | Admin @ si @ SPB2014 | Serial | 2506 | |||
Permanent link to this record | ||||||
Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
|
||||
Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | |||
Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 1090 - 1097 | |||
Keywords | ||||||
Abstract | Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second. |
|||||
Address | Nottingham; UK; September 2014 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | CVPR | |||
Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
Call Number | Admin @ si @ DKF2014 | Serial | 2509 | |||
Permanent link to this record | ||||||
Author | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
|
||||
Title | Color-based data augmentation for Reflectance Estimation | Type | Conference Article | |||
Year | 2018 | Publication | 26th Color Imaging Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 284-289 | |||
Keywords | ||||||
Abstract | Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. | |||||
Address | Vancouver; November 2018 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | CIC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ SSB2018a | Serial | 3129 | |||
Permanent link to this record | ||||||
Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez |
|
||||
Title | Color Attributes for Object Detection | Type | Conference Article | |||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 3306-3313 | |||
Keywords | pedestrian detection | |||||
Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
|||||
Address | Providence; Rhode Island; USA; | |||||
Corporate Author | Thesis | |||||
Publisher | IEEE Xplore | Place of Publication | Editor | |||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
Area | Expedition | Conference | CVPR | |||
Notes | ADAS; CIC; | Approved | no | |||
Call Number | Admin @ si @ KRW2012 | Serial | 1935 | |||
Permanent link to this record | ||||||
Author | Naila Murray; Luca Marchesotti; Florent Perronnin |
|
||||
Title | AVA: A Large-Scale Database for Aesthetic Visual Analysis | Type | Conference Article | |||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 2408-2415 | |||
Keywords | ||||||
Abstract | With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks | |||||
Address | Providence, Rhode Islan | |||||
Corporate Author | Thesis | |||||
Publisher | IEEE Xplore | Place of Publication | Editor | |||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
Area | Expedition | Conference | CVPR | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ MMP2012a | Serial | 2025 | |||
Permanent link to this record | ||||||
Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
|
||||
Title | Names and Shades of Color for Intrinsic Image Estimation | Type | Conference Article | |||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 278-285 | |||
Keywords | ||||||
Abstract | In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. | |||||
Address | Providence, Rhode Island | |||||
Corporate Author | Thesis | |||||
Publisher | IEEE Xplore | Place of Publication | Editor | |||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
Area | Expedition | Conference | CVPR | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ SPB2012 | Serial | 2026 | |||
Permanent link to this record | ||||||
Author | Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
|
||||
Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | |||
Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | |||||
Address | Nottingham; UK; September 2014 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | BMVC | |||
Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | |||
Call Number | Admin @ si @ RWB2014 | Serial | 2508 | |||
Permanent link to this record | ||||||
Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell |
|
||||
Title | Portmanteau Vocabularies for Multi-Cue Image Representation | Type | Conference Article | |||
Year | 2011 | Publication | 25th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | ||
Volume | Issue | Pages | ||||
Keywords | ||||||
Abstract | We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation | |||||
Address | ||||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | NIPS | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ KWB2011 | Serial | 1865 | |||
Permanent link to this record | ||||||
Author | Ivet Rafegas; Maria Vanrell |
|
||||
Title | Color spaces emerging from deep convolutional networks | Type | Conference Article | |||
Year | 2016 | Publication | 24th Color and Imaging Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 225-230 | |||
Keywords | ||||||
Abstract | Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
|||||
Address | San Diego; USA; November 2016 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | CIC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RaV2016a | Serial | 2894 | |||
Permanent link to this record | ||||||
Author | Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
|
||||
Title | Harmony Potentials for Joint Classification and Segmentation | Type | Conference Article | |||
Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 3280–3287 | |||
Keywords | ||||||
Abstract | Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. | |||||
Address | San Francisco CA, USA | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | ||
Area | Expedition | Conference | CVPR | |||
Notes | ADAS;CIC;ISE | Approved | no | |||
Call Number | ADAS @ adas @ GBW2010 | Serial | 1296 | |||
Permanent link to this record | ||||||
Author | Naila Murray; Luca Marchesotti; Florent Perronnin |
|
||||
Title | Learning to Rank Images using Semantic and Aesthetic Labels | Type | Conference Article | |||
Year | 2012 | Publication | 23rd British Machine Vision Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 110.1-110.10 | |||
Keywords | ||||||
Abstract | Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one. | |||||
Address | Guildford, London | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | 1-901725-46-4 | Medium | |||
Area | Expedition | Conference | BMVC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ MMP2012b | Serial | 2027 | |||
Permanent link to this record | ||||||
Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg |
|
||||
Title | Scale Coding Bag-of-Words for Action Recognition | Type | Conference Article | |||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | ||
Volume | Issue | Pages | 1514-1519 | |||
Keywords | ||||||
Abstract | Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods. |
|||||
Address | Stockholm; August 2014 | |||||
Corporate Author | Thesis | |||||
Publisher | Place of Publication | Editor | ||||
Language | Summary Language | Original Title | ||||
Series Editor | Series Title | Abbreviated Series Title | ||||
Series Volume | Series Issue | Edition | ||||
ISSN | ISBN | Medium | ||||
Area | Expedition | Conference | ICPR | |||
Notes | CIC; LAMP; 601.240; 600.074; 600.079 | Approved | no | |||
Call Number | Admin @ si @ KWB2014 | Serial | 2450 | |||
Permanent link to this record |