Region and constellations based categorization of images with unsupervised graph learning

作     者：Lozano, M. A. Escolano, F. Bonev, B. Suau, P. Aguilar, W. Saez, J. M. Cazorla, M. A. 

作者机构：Univ Alicante Dpto Ciencia Computac & Inteligencia Artificial E-03080 Alicante Spain Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Mexico City 04510 DF Mexico 

出 版 物：《IMAGE AND VISION COMPUTING》 (图像与视觉计算)

年 卷 期：2009年第27卷第7期

页      面：960-978页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：Ministerio de Educación y Ciencia 

主　　题：Image categorization Clustering of graphs EM algorithms 

摘      要：in this paper, we address the problem of image categorization with a fast novel method based on the unsupervised clustering of graphs in the context of both region-based segmentation and the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of either Softassign or fast matching with graph transformations. We present two realistic applications and their experimental results: categorization of image segmentations and visual localization. We compare our graph prototypes with the set median graphs. Our results reveal that, on the one hand, structure extracted from images improves appearance-based visual localization accuracy. On the other hand, we show that the cost of our central graph clustering algorithm is the cost of a pairwise algorithm. We also discuss how the method scales with an increasing amount of images. In addition, we address the scientific question of what are the bounds of structural learning for categorization. Our in-depth experiments both for region-based and feature-based image categorization, will show that such bounds depend hardly on structural variability. (C) 2008 Elsevier B.V. All rights reserved.