Although, color is one of the most visually distinguishable visual properties, color alone is not enough to describe the content of images. The spatial organization of the different color regions also play an important role. In this paper, we propose and evaluate a new descriptor that combines information about color and about its spatial arrangement in an image. Moreover, the mechanism used to compute the descriptor provides support for partial matching of images and for the development of efficient retrieval systems. We first describe the spatial arrangement of the color regions using a topological graph, where vertices represent the color regions and edges represent connections between regions and also the color differences between them. To compute the descriptor from this graph representation we use the spectral graph theory, avoiding the need for direct graph comparison. We performed various experimental evaluations to compare the accuracy of our new descriptor with descriptors based only on color, based only on topological information and a combination of both.

In this paper we propose a new descriptor for content-based image retrieval that explores the locality of features. We propose to extend the bag-of-visual-words method by weighting the visual words according to their spatial locality in terms of foreground and background by using fuzzy saliency models. We evaluated our method using databases that obtains images with different conditions of illumination, color, rigid and scale transformations, and changes of the background. The analysis of the results demonstrated that our proposal presents significant improvements over competitive approaches.

Information derived from the cross-references among the documents in a hyperlinked environment, usually referred to as link information, is considered important since it can be used to effectively improve document retrieval. Depending on the retrieval strategy, link information can be local or global. Local link information is derived from the set of documents returned as answers to the current user query. Global link information is derived from all the documents in the collection. In this work, we investigate how the use of local link information compares to the use of global link information. For the comparison, we run a series of experiments using a large document collection extracted from the Web. For our reference collection, the results indicate that the use of local link information improves precision by 74%. When global link information is used, precision improves by 35%. However, when only the first 10 documents in the ranking are considered, the average gain in precision obtained with the use of global link information is higher than the gain obtained with the use of local link information. This is an interesting result since it provides insight and justification for the use of global link information in major Web search engines, where users are mostly interested in the first 10 answers. Furthermore, global information can be computed in the background, which allows speeding up query processing.

This work presents an information retrieval model developed to deal with hyperlinked environments. The model is based on belief networks and provides a framework for combining information extracted from the content of the documents with information derived from cross-references among the documents. The information extracted from the content of the documents is based on statistics regarding the keywords in the collection and is one of the basis for traditional information retrieval (IR) ranking algorithms. The information derived from cross-references among the documents is based on link references in a hyperlinked environment and has received increased attention lately due to the success of the Web. We discuss a set of strategies for combining these two types of sources of evidential information and experiment with them using a reference collection extracted from the Web. The results show that this type of combination can improve the retrieval performance without requiring any extra information from the users at query time. In our experiments, the improvements reach up to 59% in terms of average precision figures.