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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
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Author	Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras
Title	Light Direction and Color Estimation from Single Image with Deep Regression			Type	Conference Article
Year	2020	Publication	London Imaging Conference	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes.
Address	Virtual; September 2020
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	LIM
Notes	CIC; 600.118; 600.140;			Approved	no
Call Number	Admin @ si @ SBV2020			Serial	3460
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Author	Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich
Title	DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition			Type	Conference Article
Year	2015	Publication	Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II	Abbreviated Journal
Volume	9475	Issue		Pages	463-473
Keywords	Projector-camera systems; Feature descriptors; Object recognition
Abstract	Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection.
Address
Corporate Author				Thesis
Publisher	Springer International Publishing	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title	LNCS
Series Volume		Series Issue		Edition
ISSN	0302-9743	ISBN	978-3-319-27862-9	Medium
Area		Expedition		Conference	ISVC
Notes	CIC			Approved	no
Call Number	Admin @ si @ SMG2015			Serial	2736
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Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title	Perceptual color texture codebooks for retrieving in highly diverse texture datasets			Type	Conference Article
Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	866–869
Keywords
Abstract	Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel.
Address	Istanbul (Turkey)
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	1051-4651	ISBN	978-1-4244-7542-1	Medium
Area		Expedition		Conference	ICPR
Notes	CIC			Approved	no
Call Number	CAT @ cat @ ASV2010b			Serial	1426
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Author	Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras
Title	Intrinsic Image Evaluation On Synthetic Complex Scenes			Type	Conference Article
Year	2013	Publication	20th IEEE International Conference on Image Processing	Abbreviated Journal
Volume		Issue		Pages	285 - 289
Keywords
Abstract	Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.
Address	Melbourne; Australia; September 2013
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	ICIP
Notes	CIC; 600.048; 600.052; 600.051			Approved	no
Call Number	Admin @ si @ BSW2013			Serial	2264
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Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title	3D Texton Spaces for color-texture retrieval			Type	Conference Article
Year	2010	Publication	7th International Conference on Image Analysis and Recognition	Abbreviated Journal
Volume	6111	Issue		Pages	354–363
Keywords
Abstract	Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.
Address
Corporate Author				Thesis
Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	A.C. Campilho and M.S. Kamel
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title	LNCS
Series Volume		Series Issue		Edition
ISSN	0302-9743	ISBN	978-3-642-13771-6	Medium
Area		Expedition		Conference	ICIAR
Notes	CIC			Approved	no
Call Number	CAT @ cat @ ASV2010a			Serial	1325
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Author	Ivet Rafegas; Maria Vanrell
Title	Color representation in CNNs: parallelisms with biological vision			Type	Conference Article
Year	2017	Publication	ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT).
Address	Venice; Italy; October 2017
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	ICCV-MBCC
Notes	CIC; 600.087; 600.051			Approved	no
Call Number	Admin @ si @ RaV2017			Serial	2984
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title	Top-Down Color Attention for Object Recognition			Type	Conference Article
Year	2009	Publication	12th International Conference on Computer Vision	Abbreviated Journal
Volume		Issue		Pages	979 - 986
Keywords
Abstract	Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
Address	Kyoto, Japan
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	1550-5499	ISBN	978-1-4244-4420-5	Medium
Area		Expedition		Conference	ICCV
Notes	CIC			Approved	no
Call Number	CAT @ cat @ SWV2009			Serial	1196
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Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
Title	Categorical Focal Colours are Structurally Invariant Under Illuminant Changes			Type	Conference Article
Year	2011	Publication	European Conference on Visual Perception	Abbreviated Journal
Volume		Issue		Pages	196
Keywords
Abstract	The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes.
Address
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title	Perception 40	Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference	ECVP
Notes	CIC			Approved	no
Call Number	Admin @ si @ RPV2011			Serial	1867
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Author	Ivet Rafegas; Maria Vanrell
Title	Colour Visual Coding in trained Deep Neural Networks			Type	Abstract
Year	2016	Publication	European Conference on Visual Perception	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract
Address	Barcelona; Spain; August 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	ECVP
Notes	CIC			Approved	no
Call Number	Admin @ si @ RaV2016b			Serial	2895
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Author	Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga
Title	Saliency Estimation Using a Non-Parametric Low-Level Vision Model			Type	Conference Article
Year	2011	Publication	IEEE conference on Computer Vision and Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	433-440
Keywords	Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms
Abstract	Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.
Address	Colorado Springs
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	978-1-4577-0394-2	Medium
Area		Expedition		Conference	CVPR
Notes	CIC			Approved	no
Call Number	Admin @ si @ MVO2011			Serial	1757
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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
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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
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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
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