An Object Classification Approach Based on Randomized Visual Vocabulary and Clustering Aggregation
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 randomi...
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Published in | Applied Mechanics and Materials Vol. 433-435; no. Advances in Mechatronics and Control Engineering II; pp. 778 - 782 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Zurich
Trans Tech Publications Ltd
15.10.2013
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | Selected, peer reviewed papers from the 2013 2nd International Conference on Mechatronics and Control Engineering (ICMCE 2013), August 28-29, 2013, Guangzhou, China ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISBN: | 303785894X 9783037858943 |
ISSN: | 1660-9336 1662-7482 1662-7482 |
DOI: | 10.4028/www.scientific.net/AMM.433-435.778 |