Evaluation of color representation for texture analysis

Since more than 50 years texture in image material is a topic of research. Hereby, color was ignored mostly. This study compares 70 different configurations for texture analysis, using four features. For the configurations we used: (i) a gray value texture descriptor: the co-occurrence matrix and a color texture descriptor: the color correlogram, (ii) six color spaces, and (iii) several quantization schemes. A three classifier combination was used to classify the output of the configurations on the VisTex texture database. The results indicate that the use of a coarse HSV color space quantization can substantially improve texture recognition compared to various other gray and color quantization schemes

Tongue Image Analysis for Diabetes Mellitus Diagnosis Based on SOM Kohonen

Tongue diagnosis is an important diagnostic method for evaluating the condition of internal organ by looking at the image of tongue . However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, SOM Kohonen are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on 35 patients affected by Diabetes Mellitus as well as other 30 healthy volunteers, and the diagnostic results predicted by the previously trained SOM Kohonen classifiers are compared with the HOMA-B

Object knowledge modulates colour appearance

We investigated the memory colour effect for colour diagnostic artificial objects. Since knowledge about these objects and their colours has been learned in everyday life, these stimuli allow the investigation of the influence of acquired object knowledge on colour appearance. These investigations are relevant for questions about how object and colour information in high-level vision interact as well as for research about the influence of learning and experience on perception in general. In order to identify suitable artificial objects, we developed a reaction time paradigm that measures (subjective) colour diagnosticity. In the main experiment, participants adjusted sixteen such objects to their typical colour as well as to grey. If the achromatic object appears in its typical colour, then participants should adjust it to the opponent colour in order to subjectively perceive it as grey. We found that knowledge about the typical colour influences the colour appearance of artificial objects. This effect was particularly strong along the daylight axis

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Cluster Oriented Image Retrieval System with Context Based Color Feature Subspace Selection

This paper presents a cluster oriented image retrieval system with context recognition mechanism for selection subspaces of color features. Our idea to implement a context in the image retrieval system is how to recognize the most important features in the image search by connecting the user impression to the query. We apply a context recognition with Mathematical Model of Meaning (MMM) and then make a projection to the color features with a color impression metric. After a user gives a context, the MMM retrieves the highest correlated words to the context. These representative words are projected to the color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace selection, the system then clusters the image database using Pillar-Kmeans algorithm. The centroids of clustering results are used for calculating the similarity measurements to the image query. We perform our proposed system for experimental purpose with the Ukiyo-e image datasets from Tokyo Metropolitan Library for representing the Japanese cultural image collections

Boundary, Brightness, and Depth Interactions During Preattentive Representation and Attentive Recognition of Figure and Ground

This article applies a recent theory of 3-D biological vision, called FACADE Theory, to explain several percepts which Kanizsa pioneered. These include 3-D pop-out of an occluding form in front of an occluded form, leading to completion and recognition of the occluded form; 3-D transparent and opaque percepts of Kanizsa squares, with and without Varin wedges; and interactions between percepts of illusory contours, brightness, and depth in response to 2-D Kanizsa images. These explanations clarify how a partially occluded object representation can be completed for purposes of object recognition, without the completed part of the representation necessarily being seen. The theory traces these percepts to neural mechanisms that compensate for measurement uncertainty and complementarity at individual cortical processing stages by using parallel and hierarchical interactions among several cortical processing stages. These interactions are modelled by a Boundary Contour System (BCS) that generates emergent boundary segmentations and a complementary Feature Contour System (FCS) that fills-in surface representations of brightness, color, and depth. The BCS and FCS interact reciprocally with an Object Recognition System (ORS) that binds BCS boundary and FCS surface representations into attentive object representations. The BCS models the parvocellular LGN→Interblob→Interstripe→V4 cortical processing stream, the FCS models the parvocellular LGN→Blob→Thin Stripe→V4 cortical processing stream, and the ORS models inferotemporal cortex.Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100