Grey‐level co‐occurrence matrix (GLCM) is a widely used texture feature descriptor that is extracted from grey‐level images. A considerable amount of work in the literature has been done trying to combine colour with GLCM features. The aim of this study is to examine the effect of integrating colour with GLCM. To this end, 14 descriptors that have been proposed in literature based on different colour combining approaches, four Convolutional Neural Networks‐based, two aggregation‐based, and five other handcrafted descriptors have been engaged in this comparison. Five widely known data sets have been used to experimentally evaluate each descriptor. The evaluation has been conducted for image classification and image retrieval. Results indicated that there is no co‐occurrence‐matrix‐based descriptor that overcomes all the others in all data sets. Nevertheless, some descriptors have shown a remarkably higher performance than the others.

In this paper, we present a new framework for organizing image collections into structures that can be used for indexing, browsing, retrieval and summarization. Instead of using tree-based techniques which are not suitable for images, we develop a new solution that is specifically designed for image collections. We consider both low-level image content and high-level semantics in an attempt to alleviate the semantic gap encountered by many systems. The fact that our model is based on a probabilistic framework makes it possible to combine it in a natural way with probabilistic techniques developed recently for image retrieval. The structure our model generates is applied for four purposes. The first is to provide retrieval module with an index, which allows it to improve retrieval time and accuracy, while the second is to provide users with a hierarchical browsing catalog that allows them to navigate the image collection by subject. This represents an additional step towards facilitating human-computer interaction in the context of image retrieval and navigation. The third aim is to provide users with a summarization of the general content of each class in the collection, and the fourth is a retrieval mechanism. Related issues such as relevance feedback and feature selection are also addressed. The experiments at the end of the paper show that the proposed framework yields some significant improvements

In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the advantages of a probabilistic formulation with those of using both the positive example (PE) and the negative example (NE). Through interaction with the user, our algorithm learns the importance he assigns to image features, and then applies the results obtained to define similarity measures that correspond better to his judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. As for the probabilistic formulation of the problem, it presents a multitude of advantages and opens the door to more modeling possibilities that achieve a good feature selection. It makes it possible to cluster the query data into classes, choose the probability law that best models each class, model missing data, and support queries with multiple PE and/or NE classes. The basic principle of our algorithm is to assign more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The proposed algorithm was validated separately and in image retrieval context, and the experiments show that it performs a good feature selection and contributes to improving retrieval effectiveness.

We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.