%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2015/06.14.17.29
%2 sid.inpe.br/sibgrapi/2015/06.14.17.29.27
%@doi 10.1109/SIBGRAPI.2015.25
%T Partial Least Squares Image Clustering
%D 2015
%A Kloss, Ricardo Barbosa,
%A Cirne, Marcos Vinicius Mussel,
%A Silva, Samira,
%A Pedrini, Hélio,
%A Schwartz, William Robson,
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade de Campinas
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade de Campinas
%@affiliation Universidade Federal de Minas Gerais
%E Papa, João Paulo,
%E Sander, Pedro Vieira,
%E Marroquim, Ricardo Guerra,
%E Farrell, Ryan,
%B Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)
%C Salvador, BA, Brazil
%8 26-29 Aug. 2015
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Image Clustering, Partial Least Squares, Video Summarization, Shot Sampling.
%X Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one-against-all Partial Least Squares classifier to find image clusters with low redundancy (each cluster represents different visual concept) and high purity (two visual concepts should not be in the same cluster). The main goal of the proposed approach is to find groups of images in an arbitrary set of unlabeled images to convey well defined visual concepts. As a case study, we evaluate the PLSIC to the video summarization problem by means of experiments with 50 videos from various genres of the Open Video Project, comparing summaries generated by the PLSIC with other video summarization approaches found in the literature. A experimental evaluation demonstrates that the proposed method can produce very satisfactory results.
%@language en
%3 PID3763835.pdf