Home | [1–10] << 11 12 13 >> |
![]() |
Records | |||||
---|---|---|---|---|---|
Author | Xavier Otazu; J. Nuñez | ||||
Title | Algoritmo de Clasificacion no Supervisada Basado en Wavelets. | Type | Miscellaneous | ||
Year | 2001 | Publication | Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 437–440. | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address ![]() |
Lleida | ||||
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 @ ONu2001 | Serial | 147 | ||
Permanent link to this record | |||||
Author | Miquel Ferrer; Robert Benavente; Ernest Valveny; J. Garcia; Agata Lapedriza; Gemma Sanchez | ||||
Title | Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica | Type | Miscellaneous | ||
Year | 2008 | Publication | Octava Jornada sobre Aprendizaje Cooperativo, 41–46 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address ![]() |
Lleida (Spain). | ||||
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 | OR;DAG;CIC;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ FBV2008 | Serial | 955 | ||
Permanent link to this record | |||||
Author | Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell | ||||
Title | Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures | Type | Journal Article | ||
Year | 2011 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 33 | Issue | 5 | Pages | 917-930 |
Keywords | |||||
Abstract | The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. | ||||
Address ![]() |
Los Alamitos; CA; USA; | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE Computer Society | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0162-8828 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ VBW2011 | Serial | 1715 | ||
Permanent link to this record | |||||
Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
Title | Improving Color Constancy by Photometric Edge Weighting | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 34 | Issue | 5 | Pages | 918-929 |
Keywords | |||||
Abstract | : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. | ||||
Address ![]() |
Los Alamitos; 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 | 0162-8828 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC;ISE | Approved | no | ||
Call Number | Admin @ si @ GGW2012 | Serial | 1850 | ||
Permanent link to this record | |||||
Author | Robert Benavente; Maria Vanrell | ||||
Title | Parametrizacion del Espacio de Categorias de Color | Type | Miscellaneous | ||
Year | 2007 | Publication | Proceedings del VIII Congreso Nacional del Color | Abbreviated Journal | |
Volume | Issue | Pages | 77–78 | ||
Keywords | |||||
Abstract | |||||
Address ![]() |
Madrid (Spain) | ||||
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 | CNC’07 | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ BeV2007 | Serial | 905 | ||
Permanent link to this record | |||||
Author | Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich | ||||
Title | Image Segmentation in the Presence of Shadows and Highligts | Type | Conference Article | ||
Year | 2008 | Publication | 10th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 5305 | Issue | Pages | 1–14 | |
Keywords | |||||
Abstract | |||||
Address ![]() |
Marseille (France) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCV | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ VVB2008b | Serial | 1013 | ||
Permanent link to this record | |||||
Author | Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet | ||||
Title | Towards multispectral data acquisition with hand-held devices | Type | Conference Article | ||
Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 2053 - 2057 | ||
Keywords | Multispectral; mobile devices; color measurements | ||||
Abstract | We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
||||
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; DAG; 600.048 | Approved | no | ||
Call Number | Admin @ si @ KWK2013b | Serial | 2265 | ||
Permanent link to this record | |||||
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 | ||
Permanent link to this record | |||||
Author | Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich | ||||
Title | Perception Based Representations for Computational Colour | Type | Conference Article | ||
Year | 2011 | Publication | 3rd International Workshop on Computational Color Imaging | Abbreviated Journal | |
Volume | 6626 | Issue | Pages | 16-30 | |
Keywords | colour perception, induction, naming, psychophysical data, saliency, segmentation | ||||
Abstract | The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. | ||||
Address ![]() |
Milan, Italy | ||||
Corporate Author | Thesis | ||||
Publisher | Springer-Verlag | Place of Publication | Editor | Raimondo Schettini, Shoji Tominaga, Alain Trémeau | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-642-20403-6 | Medium | ||
Area | Expedition | Conference | CCIW | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ VMB2011 | Serial | 1733 | ||
Permanent link to this record | |||||
Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta | ||||
Title | Area Under the ROC Curve Maximization for Metric Learning | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition | ||||
Abstract | Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. | ||||
Address ![]() |
New Orleans, USA; 20 June 2022 | ||||
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 | CVPRW | ||
Notes | CIC; LAMP; | Approved | no | ||
Call Number | Admin @ si @ GAB2022 | Serial | 3700 | ||
Permanent link to this record | |||||
Author | Anna Salvatella; Maria Vanrell; Juan J. Villanueva | ||||
Title | Texture Description based on Subtexture Components, 3rd International Workshop on Texture Syntesis and Analysis | Type | Conference Article | ||
Year | 2003 | Publication | 3rd International Workshop on Texture Synthesis and Analysis, | Abbreviated Journal | |
Volume | Issue | Pages | 77–82 | ||
Keywords | |||||
Abstract | |||||
Address ![]() |
Nice | ||||
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 | 1-904410-11-1 | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ SVV2003 | Serial | 422 | ||
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 | 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 | Ivet Rafegas | ||||
Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
||||
Address ![]() |
November 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-945373-7-0 | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Raf2017 | Serial | 3100 | ||
Permanent link to this record | |||||
Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell | ||||
Title | High-Level Clothes Description Based on Colour-Texture and Structural Features | Type | Conference Article | ||
Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address ![]() |
Palma de Mallorca | ||||
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 | DAG;CIC | Approved | no | ||
Call Number | CAT @ cat @ BTL2003b | Serial | 369 | ||
Permanent link to this record | |||||
Author | Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat | ||||
Title | Discriminative Color Descriptors | Type | Conference Article | ||
Year | 2013 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2866 - 2873 | ||
Keywords | |||||
Abstract | Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200. | ||||
Address ![]() |
Portland; Oregon; June 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 | 1063-6919 | ISBN | Medium | ||
Area | Expedition | Conference | CVPR | ||
Notes | CIC; 600.048 | Approved | no | ||
Call Number | Admin @ si @ KWK2013a | Serial | 2262 | ||
Permanent link to this record | |||||
Author | Bojana Gajic; Eduard Vazquez; Ramon Baldrich | ||||
Title | Evaluation of Deep Image Descriptors for Texture Retrieval | Type | Conference Article | ||
Year | 2017 | Publication | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) | Abbreviated Journal | |
Volume | Issue | Pages | 251-257 | ||
Keywords | Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation | ||||
Abstract | The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures. |
||||
Address ![]() |
Porto, Portugal; 27 February – 1 March 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 | VISIGRAPP | ||
Notes | CIC; 600.087 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3710 | ||
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 |