In Content-based Image Retrieval (CBIR) systems, ranking accurately collection images is of
great relevance. Users are interested in the returned images placed at the first positions …

Performing effective image retrieval tasks, capable of exploiting the underlying structure of
datasets still constitutes a challenge research scenario. This paper proposes a novel …

Abstract Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis,
ie, they measure the similarity between pairs of images, ignoring the rich information …

Accurately ranking images and multimedia objects are of paramount relevance in many
retrieval and learning tasks. Manifold learning methods have been investigated for ranking …

Effectively measuring the similarity among images is a challenging problem in image
retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents …

Contextual information, defined in terms of the proximity of feature vectors in a feature space,
has been successfully used in the construction of search services. These search systems …

In this paper, we present an unsupervised distance learning approach for improving the
effectiveness of image retrieval tasks. We propose a Reciprocal kNN Graph algorithm that …

In spite of the huge advances in supervised learning, the common requirement for extensive
labeled datasets represents a severe bottleneck. In this scenario, other learning paradigms …

One of the main concerns of the wide use of video surveillance is the loss of individual
privacy. Individuals who are not suspects need not be identified on camera recordings …

Impressive advances in acquisition and sharing technologies have made the growth of
multimedia collections and their applications almost unlimited. However, the opposite is true …