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Author	Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga
Title	Low-level SpatioChromatic Grouping for Saliency Estimation			Type	Journal Article
Year	2013	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
Volume	35	Issue	11	Pages	2810-2816
Keywords
Abstract	We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics.
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	0162-8828	ISBN		Medium
Area		Expedition		Conference
Notes	CIC; 600.051; 600.052; 605.203			Approved	no
Call Number	Admin @ si @ MVO2013			Serial	2289
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Author	Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras
Title	Intrinsic Image Evaluation On Synthetic Complex Scenes			Type	Conference Article
Year	2013	Publication	20th IEEE International Conference on Image Processing	Abbreviated Journal
Volume		Issue		Pages	285 - 289
Keywords
Abstract	Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.
Address	Melbourne; Australia; September 2013
Corporate Author				Thesis
Publisher		Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN		ISBN		Medium
Area		Expedition		Conference	ICIP
Notes	CIC; 600.048; 600.052; 600.051			Approved	no
Call Number	Admin @ si @ BSW2013			Serial	2264
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Author	Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title	Modulating Shape Features by Color Attention for Object Recognition			Type	Journal Article
Year	2012	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
Volume	98	Issue	1	Pages	49-64
Keywords
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.
Address
Corporate Author				Thesis
Publisher	Springer Netherlands	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	CIC			Approved	no
Call Number	Admin @ si @ KWV2012			Serial	1864
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Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez
Title	Color Attributes for Object Detection			Type	Conference Article
Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	3306-3313
Keywords	pedestrian detection
Abstract	State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.
Address	Providence; Rhode Island; USA;
Corporate Author				Thesis
Publisher	IEEE Xplore	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1063-6919	ISBN	978-1-4673-1226-4	Medium
Area		Expedition		Conference	CVPR
Notes	ADAS; CIC;			Approved	no
Call Number	Admin @ si @ KRW2012			Serial	1935
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Author	Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell
Title	Spectral sharpening by spherical sampling			Type	Journal Article
Year	2012	Publication	Journal of the Optical Society of America A	Abbreviated Journal	JOSA A
Volume	29	Issue	7	Pages	1199-1210
Keywords
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.
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	1084-7529	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ FVS2012			Serial	2000
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Author	Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson
Title	A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation			Type	Journal Article
Year	2012	Publication	Journal of Vision	Abbreviated Journal	VSS
Volume	12	Issue	6 (7)	Pages	1-14
Keywords
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.
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	Admin @ si @ VOV2012			Serial	1998
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Author	Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous
Title	Color Constancy by Category Correlation			Type	Journal Article
Year	2012	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
Volume	21	Issue	4	Pages	1997-2007
Keywords
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.
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			Approved	no
Call Number	Admin @ si @ VVB2012			Serial	1999
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Author	Javier Vazquez; Robert Benavente; Maria Vanrell
Title	Naming constraints constancy			Type	Conference Article
Year	2012	Publication	2nd Joint AVA / BMVA Meeting on Biological and Machine Vision	Abbreviated Journal
Volume		Issue		Pages
Keywords
Abstract	Different studies have shown that languages from industrialized cultures share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018].
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	AV A
Notes	CIC			Approved	no
Call Number	Admin @ si @ VBV2012			Serial	2131
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Author	Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus
Title	Color Naming			Type	Book Chapter
Year	2012	Publication	Color in Computer Vision: Fundamentals and Applications	Abbreviated Journal
Volume		Issue	17	Pages	287-317
Keywords
Abstract
Address
Corporate Author				Thesis
Publisher	John Wiley & Sons, Ltd.	Place of Publication		Editor	Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek
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	Admin @ si @ WBV2012			Serial	2063
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Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
Title	Predicting categorical colour perception in successive colour constancy			Type	Abstract
Year	2012	Publication	Perception	Abbreviated Journal	PER
Volume	41	Issue		Pages	138
Keywords
Abstract	Colour constancy is a perceptual mechanism that seeks to keep the colour of objects relatively stable under an illumination shift. Experiments haveshown that its effects depend on the number of colours present in the scene. We studied categorical colour changes under different adaptation states, in particular, whether the colour categories seen under a chromatically neutral illuminant are the same after a shift in the chromaticity of the illumination. To do this, we developed the chromatic setting paradigm (2011 Journal of Vision11 349), which is as an extension of achromatic setting to colour categories. The paradigm exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. Our experiments were run on a CRT monitor (inside a dark room) under various simulated illuminants and restricting the number of colours of the Mondrian background to three, thus weakening the adaptation effect. Our results show a change in the colour categories present before (under neutral illumination) and after adaptation (under coloured illuminants) with a tendency for adapted colours to be less saturated than before adaptation. This behaviour was predicted by a simple affine matrix model, adjusted to the chromatic setting results.
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	0301-0066	ISBN		Medium
Area		Expedition		Conference
Notes	CIC			Approved	no
Call Number	Admin @ si @ RPV2012			Serial	2188
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Author	Jordi Roca; Maria Vanrell; C. Alejandro Parraga
Title	What is constant in colour constancy?			Type	Conference Article
Year	2012	Publication	6th European Conference on Colour in Graphics, Imaging and Vision	Abbreviated Journal
Volume		Issue		Pages	337-343
Keywords
Abstract	Color constancy refers to the ability of the human visual system to stabilize the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy algorithms [3] for estimating the scene illuminant.
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	9781622767014	Medium
Area		Expedition		Conference	CGIV
Notes	CIC			Approved	no
Call Number	RVP2012			Serial	2189
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Author	Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell
Title	Names and Shades of Color for Intrinsic Image Estimation			Type	Conference Article
Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
Volume		Issue		Pages	278-285
Keywords
Abstract	In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance.
Address	Providence, Rhode Island
Corporate Author				Thesis
Publisher	IEEE Xplore	Place of Publication		Editor
Language		Summary Language		Original Title
Series Editor		Series Title		Abbreviated Series Title
Series Volume		Series Issue		Edition
ISSN	1063-6919	ISBN	978-1-4673-1226-4	Medium
Area		Expedition		Conference	CVPR
Notes	CIC			Approved	no
Call Number	Admin @ si @ SPB2012			Serial	2026
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Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
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	Admin @ si @ ASV2012			Serial	1827
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Author	Susana Alvarez; Maria Vanrell
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	Admin @ si @ AlV2012a			Serial	2130
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