Content Based Retrieval

Several applications, such as digital libraries, medicine, biodiversity information systems, deal with large image collections.
Thus, it is essential to provide efficient and effective means to retrieve images. This is the objective of the so-called content-based image retrieval (CBIR) systems. In these systems, the searching process consists in, for a given image, computing the most similar images stored in the database. The searching process relies on the use of image descriptors. A descriptor is composed by two functions: feature vector extraction and similarity computation. The feature vectors encode image properties, like color, texture, and shape. Therefore, the similarity between two images is computed as a function of their feature vectors distance.
PATREO group is interested in the development of new image feature descriptors, ranking aggregation techniques and indexing structures for large-scale image retrieval/classification.

Some relevant work in this topic are:

F. A. Faria, D. C. G. Pedronette, J. A. dos Santos, A. R. Rocha, and R. da S. Torres. Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images. IEEE Journal of Selected Topics on Earth Observations and Remote Sensing, v. 7, p. 1103-1115, 2014.
A. T. da Silva, J. A. dos Santos, A. X. Falcão, R. da S. Torres, L. P. Magalhães. Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning. Computer Vision and Image Understanding (Print), v. 116, p. 510-523, 2012..
C. D. Ferreira, J. A. dos Santos, R. da S. Torres, M. A. Gonçalvez, R. C. Rezende, W. Fan. Relevance Feedback based on Genetic Programming for Image Retrieval. Pattern Recognition Letters, v. 32, p. 27-37, 2011.