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Article Information:
Evaluating Classification Strategies in Bag of SIFT Feature Method for Animal Recognition
Leila Mansourian, Muhamad Taufik Abdullah, Lilli Nurliyana Abdullah and Azreen Azman
Corresponding Author: Muhamad Taufik Abdullah
Submitted: October 29, 2014
Accepted: December 27, 2014
Published: August 15, 2015 |
Abstract:
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These days automatic image annotation is an important topic and several efforts are made to solve the semantic gap problem which is still an open issue. Also, Content Based Image Retrieval (CBIR) cannot solve this problem. One of the efficient and effective models for solving the semantic gap and visual recognition and retrieval is Bag of Feature (BoF) model which can quantize local visual features like SIFT perfectly. In this study our aim is to investigate the potential usage of Bag of SIFT Feature in animal recognition. Also, we specified which classification method is better for animal pictures.
Key words: Bag of feature, Content Based Image Retrieval (CBIR), feature quantization, image annotation, SIFT feature, Support Vector Machines (SVM),
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Cite this Reference:
Leila Mansourian, Muhamad Taufik Abdullah, Lilli Nurliyana Abdullah and Azreen Azman, . Evaluating Classification Strategies in Bag of SIFT Feature Method for Animal Recognition. Research Journal of Applied Sciences, Engineering and Technology, (11): 1266-1272.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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