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Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
High-Level Clothes Description Based on Color-Texture and Structural Features |
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Book Chapter |
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2003 |
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Lecture Notes in Computer Science |
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Volume |
2652 |
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108–116 |
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This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. |
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Springer-Verlag |
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DAG;CIC |
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no |
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CAT @ cat @ BTL2003a |
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368 |
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Author |
Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation |
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Journal Article |
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2012 |
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International Journal of Computer Vision |
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IJCV |
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96 |
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1 |
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83-102 |
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The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimplied model since multiple classes can be reasonably expected to appear within large regions. This simplied model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an eective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21. |
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0920-5691 |
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ISE;CIC;ADAS |
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Admin @ si @ BGW2012 |
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1718 |
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Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Harmony Potentials for Joint Classification and Segmentation |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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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. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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Author |
Jose Carlos Rubio |
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Graph matching based on graphical models with application to vehicle tracking and classification at night |
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Report |
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2009 |
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CVC Technical Report |
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144 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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Admin @ si @ Rub2009 |
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2398 |
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Author |
Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Gaze control in a binocular robot systems |
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1999 |
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Barcelona |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ RVV1999b |
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41 |
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Author |
Robert Benavente; Maria Vanrell |
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Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
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Miscellaneous |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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Aachen (Germany) |
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CAT @ cat @ BeV2004 |
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441 |
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Author |
M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez |
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Fusion of different spatial and spectral resolution images: development, apllication and comparison of new methods based on wavelets. |
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2002 |
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Proceedings of the 1st. International Symposium Recent Advances in Quantitative Remote Sensing. |
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CIC |
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CAT @ cat @ GOF2002 |
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291 |
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Author |
O. Fors; Xavier Otazu; J. Nuñez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. |
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Miscellaneous |
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2001 |
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Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. |
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Lleida |
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CIC |
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CAT @ cat @ FON2001 |
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94 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Fusing Color and Shape for Bag-of-Words Based Object Recognition |
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Conference Article |
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2013 |
Publication |
4th Computational Color Imaging Workshop |
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7786 |
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25-34 |
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Object Recognition; color features; bag-of-words; image classification |
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In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. |
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Chiba; Japan; March 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36699-4 |
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CCIW |
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CIC; 600.048 |
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Admin @ si @ WeK2013 |
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2283 |
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Author |
Fernando Lopez; J.M. Valiente; Ramon Baldrich; Maria Vanrell |
![goto web page (via DOI) doi](img/doi.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Fast surface grading using color statistics in the CIELab space |
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Conference Article |
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2005 |
Publication |
Pattern Recognition and Image Analysis. IbPRIA 2005 |
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LNCS 3523 |
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66-673 |
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Germany |
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LNCS |
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IbPRIA |
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CIC |
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CAT @ cat @ LVB2005 |
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641 |
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Author |
Maria Vanrell |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Exploring the space of behaviour of a texture perception algorithm |
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1997 |
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CVC Technical Report #12 |
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CVC (UAB) |
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CIC |
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CAT @ cat @ Van1997 |
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523 |
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Author |
Ricard Balague |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Exploring the combination of color cues for intrinsic image decomposition |
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2014 |
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CVC Technical Report |
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178 |
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Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. |
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UAB; September 2014 |
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Master's thesis |
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CIC; 600.074 |
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Admin @ si @ Bal2014 |
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2579 |
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Author |
Ivet Rafegas |
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Exploring Low-Level Vision Models. Case Study: Saliency Prediction |
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2013 |
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CVC Technical Report |
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175 |
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CIC |
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Admin @ si @ Raf2013 |
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2409 |
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Author |
Francesc Tous; Maria Vanrell; Ramon Baldrich |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Exploring Colour Constancy Solutions. |
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Miscellaneous |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging, and Vision, 24:29 |
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Aachen (Germany) |
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CIC |
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no |
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CAT @ cat @ TVB2004 |
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452 |
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Author |
Bojana Gajic; Eduard Vazquez; Ramon Baldrich |
![goto web page url](img/www.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Evaluation of Deep Image Descriptors for Texture Retrieval |
Type |
Conference Article |
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2017 |
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Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) |
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251-257 |
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Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation |
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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. |
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Porto, Portugal; 27 February – 1 March 2017 |
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VISIGRAPP |
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CIC; 600.087 |
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3710 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg |
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Evaluating the impact of color on texture recognition |
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2013 |
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15th International Conference on Computer Analysis of Images and Patterns |
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8047 |
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154-162 |
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Color; Texture; image representation |
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State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets. |
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York; UK; August 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40260-9 |
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CIC; 600.048 |
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Admin @ si @ KWA2013 |
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2263 |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Estimation of Fuzzy Sets for Computational Colour Categorization |
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2004 |
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Color Research and Application, 29(5):342–353 (IF: 0.739) |
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Hassan Ahmed Sial |
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Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this thesis, we explore how to estimate the effects of the light interacting with the scene objects from a single image. To achieve this goal, we focus on recovering intrinsic components like reflectance, shading, or light properties such as color and position using deep architectures. The success of these approaches relies on training on large and diversified image datasets. Therefore, we present several contributions on this such as: (a) a data-augmentation technique; (b) a ground-truth for an existing multi-illuminant dataset; (c) a family of synthetic datasets, SID for Surreal Intrinsic Datasets, with diversified backgrounds and coherent light conditions; and (d) a practical pipeline to create hybrid ground-truths to overcome the complexity of acquiring realistic light conditions in a massive way. In parallel with the creation of datasets, we trained different flexible encoder-decoder deep architectures incorporating physical constraints from the image formation models.
In the last part of the thesis, we apply all the previous experience to two different problems. Firstly, we create a large hybrid Doc3DShade dataset with real shading and synthetic reflectance under complex illumination conditions, that is used to train a two-stage architecture that improves the character recognition task in complex lighting conditions of unwrapped documents. Secondly, we tackle the problem of single image scene relighting by extending both, the SID dataset to present stronger shading and shadows effects, and the deep architectures to use intrinsic components to estimate new relit images. |
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September 2021 |
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Ph.D. thesis |
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IMPRIMA |
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Maria Vanrell;Ramon Baldrich |
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978-84-122714-8-5 |
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CIC; |
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Admin @ si @ Sia2021 |
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3607 |
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