Vision-based representation and recognition of human activities in image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013von Samy Sadek Mohamed Bakhee

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

USING TSALLIS ENTROPY INTO A BAYESIAN NETWORK FOR CBIR

This paper presents a Bayesian Network model for Content-Based Image Retrieval (CBIR). In the explanation and test of this work, only two images features (semantic evidences) are involved: color and shape (from gradients of directions). However, one of the main advantages of the proposed strategy is its easy extension to several evidences. Considering the precision with which the images are retrieved, to highlight the evidences that generate the best results, we have introduced the use of Nonextensive Entropy. This concept extends the Shannon’s classic theory of entropy for Information Systems. Experimental results show that may be a link between the parameters of the Tsalli’s nonextensive entropy and the precision with which the images are retrieved from the database. In some cases, we have obtained up to 30 % in terms of average precision. 1