Since many real-world concepts are associated with colour, for example danger with red, linguistic information is often complimented with the use of appropriate colours in information visualization and product marketing. Yet, there is no comprehensive resource that captures concept-colour associations. We present a method to create a large word-colour association lexicon by crowdsourcing. We focus especially on abstract concepts and emotions to show that even though they cannot be physically visualized, they too tend to have strong colour associations. Finally, we show how word-colour associations manifest themselves in language, and quantify usefulness of co-occurrence and polarity cues in automatically detecting colour associations.
The MRC Psycholinguistic Database @cite_15 has, among other information, the imageability ratings for 9240 words. http: www.psy.uwa.edu.au mrcdatabase uwa .htm The imageability rating is a score given by human judges that reflects how easy it is to visualize the concept. It is a scale from 100 (very hard to visualize) to 700 (very easy to visualize). We use the ratings in our experiments to determine whether there is a correlation between imageability and strength of colour association.
@cite_15
1985449126
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http: www.cns.nyu.edu spl sim lcv ssim . Automatic image quality assessment (IQA) attempts to use computational models to measure the image quality in consistency with subjective ratings. In the past decades, dozens of IQA models have been proposed. Though some of them can predict subjective image quality accurately, their computational costs are usually very high. To meet real-time requirements, in this paper, we propose a novel fast and effective IQA index, namely spectral residual based similarity (SR-SIM), based on a specific visual saliency model, spectral residual visual saliency. SR-SIM is designed based on the hypothesis that an image's visual saliency map is closely related to its perceived quality. Extensive experiments conducted on three large-scale IQA datasets indicate that SR-SIM could achieve better prediction performance than the other state-of-the-art IQA indices evaluated. Moreover, SR-SIM can have a quite low computational complexity. The Matlab source code of SR-SIM and the evaluation results are available online at http: sse.tongji.edu.cn linzhang IQA SR-SIM SR-SIM.htm. Abstract In this paper we present the results of two experiments. The first is on the categorization of texture words in the English language. The goal was to determine whether there is a common basis for subjects' groupings of words related to visual texture, and if so, to identify the underlying dimensions used to categorize those words. Eleven major clusters were identified through hierarchical cluster analysis, ranging from ‘random’ to ‘repetitive’. These clusters remained intact in a multidimensional scaling solution. The stress for a three-dimensional solution obtained through multidimensional scaling was 0.18, meaning that 82 of the variance in the data is explained through the use of three dimensions. It appears that the major dimensions of texture descriptors are repetitive versus nonrepetitive; linearly oriented versus circularly oriented; and simple versus complex. In the second experiment we measured the strength of association between texture words and texture images. The goal was to determine whether there is any systematic correspondence between the domains of texture words and texture images. Pearson's coefficient of contingency, a measure of the strength of association, was found to be 0.63 for words corresponding to given images and 0.56 for images corresponding to given words. Thus the texture categories in the verbal space and those in the visual space are strongly tied. In sum, our two experiments show (a) that despite the tremendous variety in the words we have to describe textures, there is an underlying structure to the lexical space which can be derived from the experimental data; and (b) that the association between a category of words and a category of images was strongest when both categories represent the same underlying property. This suggests that subjects' organizations of texture terms are systematically tied to their organization of texture images. Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we define background firing rate (BFR) for each sparse feature, and then we propose to use feature activation rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute, also with satisfied performance. Experiments on human eye fixations and psychological patterns demonstrate the effectiveness and robustness of our proposed method.