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Author Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez edit  url
openurl 
  Title Enhancing spatio-chromatic representation with more-than-three color coding for image description Type Journal Article
  Year 2017 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 34 Issue (up) 5 Pages 827-837  
  Keywords  
  Abstract Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.  
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  Area Expedition Conference  
  Notes CIC; 600.087 Approved no  
  Call Number Admin @ si @ RVB2017 Serial 2892  
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Author Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin edit  url
openurl 
  Title Towards automatic and flexible concept transfer Type Journal Article
  Year 2012 Publication Computers and Graphics Abbreviated Journal CG  
  Volume 36 Issue (up) 6 Pages 622–634  
  Keywords  
  Abstract This paper introduces a novel approach to automatic, yet flexible, image concepttransfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The presented method modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This method is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. Our framework is flexible for two reasons. First, the user may select one of two modalities to map input image chromaticities to target concept chromaticities depending on the level of photo-realism required. Second, the user may adjust the intensity level of the concepttransfer to his/her liking with a single parameter. The proposed method uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. Results show that our approach yields transferred images which effectively represent concepts as confirmed by a user study.  
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  Series Volume Series Issue Edition  
  ISSN 0097-8493 ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ MSM2012 Serial 2002  
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Author Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg edit  doi
openurl 
  Title Painting-91: A Large Scale Database for Computational Painting Categorization Type Journal Article
  Year 2014 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 25 Issue (up) 6 Pages 1385-1397  
  Keywords  
  Abstract Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.  
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  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ KBW2014 Serial 2510  
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Author Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson edit  url
doi  openurl
  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 (up) 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.  
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  Notes CIC Approved no  
  Call Number Admin @ si @ VOV2012 Serial 1998  
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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 (up) 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.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ CVM2021 Serial 3595  
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