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Author Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez edit   pdf
url  doi
openurl 
Title Discriminative Compact Pyramids for Object and Scene Recognition Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR  
Volume 45 Issue 4 Pages 1627-1636  
Keywords  
Abstract Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.  
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 0031-3203 ISBN Medium  
Area Expedition Conference  
Notes ISE; CAT;CIC Approved no  
Call Number (down) Admin @ si @ EKW2012 Serial 1807  
Permanent link to this record
 
 
Author Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras edit   pdf
openurl 
Title Intrinsic Decomposition of Document Images In-the-Wild Type Conference Article
Year 2020 Publication 31st British Machine Vision Conference Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised
methods on real data are impossible due to the large amount of data needed. Hence, the
current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW.		  
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 BMVC  
Notes CIC; 600.087; 600.140; 600.118 Approved no  
Call Number (down) Admin @ si @ DSM2020 Serial 3461  
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Author M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer edit   pdf
doi  openurl
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 (down) Admin @ si @ DKF2014 Serial 2509  
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Author Maria del Camp Davesa edit  openurl
Title Human action categorization in image sequences Type Report
Year 2011 Publication CVC Technical Report Abbreviated Journal  
Volume 169 Issue Pages  
Keywords  
Abstract  
Address Bellaterra (Spain)  
Corporate Author Computer Vision Center Thesis Master's 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;CIC Approved no  
Call Number (down) Admin @ si @ Dav2011 Serial 1934  
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Author Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte edit   pdf
url  openurl
Title Perceptual image enhancement for smartphone real-time applications Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Abbreviated Journal  
Volume Issue Pages 1848-1858  
Keywords  
Abstract Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.  
Address Waikoloa; Hawai; USA; January 2023  
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 WACV  
Notes MACO; CIC Approved no  
Call Number (down) Admin @ si @ CVV2023 Serial 3900  
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Author Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío edit   pdf
url  doi
openurl 
Title Matching visual induction effects on screens of different size Type Journal Article
Year 2021 Publication Journal of Vision Abbreviated Journal JOV  
Volume 21 Issue 6(10) Pages 1-22  
Keywords  
Abstract In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.  
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  
Notes CIC Approved no  
Call Number (down) Admin @ si @ CVM2021 Serial 3595  
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Author Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown edit   pdf
openurl 
Title Noise Prism: A Novel Multispectral Visualization Technique Type Journal Article
Year 2021 Publication 31st Color and Imaging Conference Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique.		  
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 CIC  
Notes MACO; CIC Approved no  
Call Number (down) Admin @ si @ CVL2021 Serial 4000  
Permanent link to this record
 
 
Author Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte edit   pdf
url  openurl
Title NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement Type Conference Article
Year 2024 Publication 38th AAAI Conference on Artificial Intelligence Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs.  
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 AAAI  
Notes CIC; MACO Approved no  
Call Number (down) Admin @ si @ CVB2024 Serial 3872  
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Author Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras edit   pdf
doi  openurl
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 (down) Admin @ si @ BSW2013 Serial 2264  
Permanent link to this record
 
 
Author Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou edit   pdf
doi  openurl
Title Multi-Illuminant Estimation with Conditional Random Fields Type Journal Article
Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
Volume 23 Issue 1 Pages 83-95  
Keywords color constancy; CRF; multi-illuminant  
Abstract Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.  
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 1057-7149 ISBN Medium  
Area Expedition Conference  
Notes CIC; LAMP; 600.074; 600.079 Approved no  
Call Number (down) Admin @ si @ BRW2014 Serial 2451  
Permanent link to this record
 
 
Author Xavier Boix edit  openurl
Title Learning Conditional Random Fields for Stereo Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume 136 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona 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 (down) Admin @ si @ Boi2009 Serial 2395  
Permanent link to this record
 
 
Author Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
Volume 96 Issue 1 Pages 83-102  
Keywords  
Abstract 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 oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed 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 e ective 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.		  
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 0920-5691 ISBN Medium  
Area Expedition Conference  
Notes ISE;CIC;ADAS Approved no  
Call Number (down) Admin @ si @ BGW2012 Serial 1718  
Permanent link to this record
 
 
Author Shida Beigpour; Joost Van de Weijer edit   pdf
url  doi
isbn  openurl
Title Object Recoloring Based on Intrinsic Image Estimation Type Conference Article
Year 2011 Publication 13th IEEE International Conference in Computer Vision Abbreviated Journal  
Volume Issue Pages 327 - 334  
Keywords  
Abstract Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods.  
Address Barcelona  
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 978-1-4577-1101-5 Medium  
Area Expedition Conference ICCV  
Notes CIC Approved no  
Call Number (down) Admin @ si @ BeW2011 Serial 1781  
Permanent link to this record
 
 
Author Shida Beigpour edit  openurl
Title Illumination and object reflectance modeling Type Book Whole
Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.  
Address Barcelona  
Corporate Author Thesis Ph.D. thesis  
Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Ernest Valveny  
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 (down) Admin @ si @ Bei2013 Serial 2267  
Permanent link to this record
 
 
Author Shida Beigpour edit  openurl
Title Physics-based Reflectance Estimation Applied to Recoloring Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume 137 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona 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 (down) Admin @ si @ Bei2009 Serial 2396  
Permanent link to this record
 
 
Author Ricard Balague edit  openurl
Title Exploring the combination of color cues for intrinsic image decomposition Type Report
Year 2014 Publication CVC Technical Report Abbreviated Journal  
Volume 178 Issue Pages  
Keywords  
Abstract 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.  
Address UAB; September 2014  
Corporate Author Thesis Master's 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; 600.074 Approved no  
Call Number (down) Admin @ si @ Bal2014 Serial 2579  
Permanent link to this record
 
 
Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu edit   pdf
url  doi
openurl 
Title Low-dimensional and Comprehensive Color Texture Description Type Journal Article
Year 2012 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
Volume 116 Issue I Pages 54-67  
Keywords  
Abstract Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap		  
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 1077-3142 ISBN Medium  
Area Expedition Conference  
Notes CAT;CIC Approved no  
Call Number (down) Admin @ si @ ASV2012 Serial 1827  
Permanent link to this record
 
 
Author Susana Alvarez; Maria Vanrell edit   pdf
url  doi
openurl 
Title Texton theory revisited: a bag-of-words approach to combine textons Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR  
Volume 45 Issue 12 Pages 4312-4325  
Keywords  
Abstract The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.		  
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 0031-3203 ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number (down) Admin @ si @ AlV2012a Serial 2130  
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