An Object Classification Approach Based on Randomized Visual Vocabulary and Clustering Aggregation

• Published in: Applied Mechanics and Materials Vol. 433-435; no. Advances in Mechatronics and Control Engineering II; pp. 778 - 782
• Main Authors: Su, Li, Zhang, Feng, Liu, Jie Nan, Ren, Lan Fang
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 15.10.2013
• Subjects: Agglomeration; Classification; Clustering; Clusters; Dictionaries; Instability; Randomization; Stability; Support vector machines; Visual; Randomized Visual Vocabulary; Object Classification; Exact Euclidean Locality Sensitive Hashing; Clustering Aggregation
• ISBN: 303785894X; 9783037858943
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.433-435.778

Considering the problems with the conventional Bag-of-Visual-Words approaches, such as high time consumption, the synonymy and ambiguity of visual word, and instability of clustering high-dimensionality image local features, this paper presents a novel object classificaiton approach based on randomized visual vocabulary and clustering aggregation. Firstly, Exact Euclidean Locality Sensitive Hashing (E2LSH) is used to cluster local features of the training dataset, and a group of randomized visual vocabularies is constructed. Then, the randomized visual vocabularies are aggregated using clustering aggregation technique, resulting in Randomized Visual Vocabularies Aggregating Dictionary (RVVAD). Finally, the visual words histogram is generated according to the dictionary, and the Support Vector Machines are learned to accomplish image object categorization. Experimental results indicate that the expression ability of the dictionary is effectively improved, and the object classification precision is increased dramatically.