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Rahat Khan, Joost Van de Weijer, Fahad Shahbaz Khan, Damien Muselet, christophe Ducottet, & Cecile Barat. (2013). Discriminative Color Descriptors. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2866–2873).
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.
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Fahad Shahbaz Khan, Joost Van de Weijer, Sadiq Ali, & Michael Felsberg. (2013). Evaluating the impact of color on texture recognition. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8047, pp. 154–162). Springer Berlin Heidelberg.
Abstract: 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.
Keywords: Color; Texture; image representation
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Christophe Rigaud, Dimosthenis Karatzas, Joost Van de Weijer, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). Automatic text localisation in scanned comic books. In Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 814–819).
Abstract: Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comics book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.
Keywords: Text localization; comics; text/graphic separation; complex background; unstructured document
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Christophe Rigaud, Dimosthenis Karatzas, Joost Van de Weijer, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). An active contour model for speech balloon detection in comics. In 12th International Conference on Document Analysis and Recognition (pp. 1240–1244).
Abstract: Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent comic book understanding would enable a variety of new applications, including content-based retrieval and content retargeting. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. Few studies have been done in this direction. In this work we detail a novel approach for closed and non-closed speech balloon localization in scanned comic book pages, an essential step towards a fully automatic comic book understanding. The approach is compared with existing methods for closed balloon localization found in the literature and results are presented.
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Olivier Penacchio, Xavier Otazu, & Laura Dempere-Marco. (2013). A Neurodynamical Model of Brightness Induction in V1. Plos - PloS ONE, 8(5), e64086.
Abstract: Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.
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Alicia Fornes, Xavier Otazu, & Josep Llados. (2013). Show through cancellation and image enhancement by multiresolution contrast processing. In 12th International Conference on Document Analysis and Recognition (pp. 200–204).
Abstract: Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities.
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Sandra Jimenez, Xavier Otazu, Valero Laparra, & Jesus Malo. (2013). Chromatic induction and contrast masking: similar models, different goals? In Human Vision and Electronic Imaging XVIII (Vol. 8651).
Abstract: Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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David Geronimo, Joan Serrat, Antonio Lopez, & Ramon Baldrich. (2013). Traffic sign recognition for computer vision project-based learning. T-EDUC - IEEE Transactions on Education, 56(3), 364–371.
Abstract: This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.
Keywords: traffic signs
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Ivet Rafegas. (2013). Exploring Low-Level Vision Models. Case Study: Saliency Prediction (Vol. 175). Master's thesis, , .
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Xavier Boix, Josep M. Gonfaus, Joost Van de Weijer, Andrew Bagdanov, Joan Serrat, & Jordi Gonzalez. (2012). Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation. IJCV - International Journal of Computer Vision, 96(1), 83–102.
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 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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Noha Elfiky, Fahad Shahbaz Khan, Joost Van de Weijer, & Jordi Gonzalez. (2012). Discriminative Compact Pyramids for Object and Scene Recognition. PR - Pattern Recognition, 45(4), 1627–1636.
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.
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Susana Alvarez, Anna Salvatella, Maria Vanrell, & Xavier Otazu. (2012). Low-dimensional and Comprehensive Color Texture Description. CVIU - Computer Vision and Image Understanding, 116(I), 54–67.
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
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Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2012). Improving Color Constancy by Photometric Edge Weighting. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(5), 918–929.
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.
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Fahad Shahbaz Khan, Muhammad Anwer Rao, Joost Van de Weijer, Andrew Bagdanov, Maria Vanrell, & Antonio Lopez. (2012). Color Attributes for Object Detection. In 25th IEEE Conference on Computer Vision and Pattern Recognition (pp. 3306–3313). IEEE Xplore.
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.
Keywords: pedestrian detection
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Fahad Shahbaz Khan, Joost Van de Weijer, & Maria Vanrell. (2012). Modulating Shape Features by Color Attention for Object Recognition. IJCV - International Journal of Computer Vision, 98(1), 49–64.
Abstract: Bag-of-words based image representation is a successful approach for object recognition. Generally, 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, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.
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Javier Vazquez, J. Kevin O'Regan, Maria Vanrell, & Graham D. Finlayson. (2012). A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation. VSS - Journal of Vision, 12(6 (7)), 1–14.
Abstract: When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data.
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Javier Vazquez, Maria Vanrell, Ramon Baldrich, & Francesc Tous. (2012). Color Constancy by Category Correlation. TIP - IEEE Transactions on Image Processing, 21(4), 1997–2007.
Abstract: Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have
on the final color image representation. The novelty of this work is to propose
perceptual constraints that are computed on the corrected images. We define the
category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy.
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Graham D. Finlayson, Javier Vazquez, Sabine Süsstrunk, & Maria Vanrell. (2012). Spectral sharpening by spherical sampling. JOSA A - Journal of the Optical Society of America A, 29(7), 1199–1210.
Abstract: There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art.
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