Gray-level co-occurrence matrix (GLCM) is one of the most used methods for texture representation. As it can be computed only from gray-level images, a significant amount of information that could be provided by color is totally ignored. We propose a generalization of GLCM from gray level to hue saturation value color space, which we refer to as modified integrative color intensity co-occurrence matrix (MICICM). To reach such a generalization, a mapping function, which determines for each pixel value the bin it falls into, is needed. In many previous studies, this function uses a hard mapping where all pixel values that fall in a bin are considered as the same, regardless of their values. This presents a number of drawbacks. To fix them, we introduce a color and gray-level mapping scheme based on a set of weight assignment functions we propose. In our scheme, each pixel is mapped to more than one possible color (and gray-level) bin, to avoid the drawbacks of hard mapping. Although a fuzzy-based scheme has been recently proposed, our MICICM has successfully outperformed it and those of the state of the art. Our findings make several noteworthy contributions to image representation.
Relevance feedback has attracted the attention of many authors in image retrieval. However, in most work, only positive example has been considered. We think that negative example can be highly useful to better model the user's needs and specificities. In this paper, we introduce a new relevance feedback model that combines positive and negative examples for query processing and refinement. We start by explaining how negative example can help mitigating many problems in image retrieval such as similarity measures definition and feature selection. Then, we propose a new relevance feedback approach that uses positive example to perform generalization and negative example to perform specialization. When the query contains both positive and negative examples, it is processed in two steps. In the first step, only positive example is considered in order to reduce the heterogeneity of the set of images that participate in retrieval. Then, the second step considers the difference between positive and negative examples and acts on the images retained in the first step. Mathematically, the problem is formulated as simultaneously minimizing intra variance of positive and negative examples, and maximizing inter varicance. The proposed algorithm was implemented in our image retrieval system "Atlas" and tested on a collection of 10.000 images. We carried out some performance evaluation and the results were promising.
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