Research Article | OPEN ACCESS
Evaluating Classification Strategies in Bag of SIFT Feature Method for Animal Recognition
Leila Mansourian, Muhamad Taufik Abdullah, Lilli Nurliyana Abdullah and Azreen Azman
Department of Multimedia, Faculty of Computer Science and Information Technology,
Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2015 11:1266-1272
Received: October 29, 2014 | Accepted: December 27, 2014 | Published: August 15, 2015
Abstract
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.
Keywords:
Bag of feature, Content Based Image Retrieval (CBIR), feature quantization, image annotation, SIFT feature, Support Vector Machines (SVM),
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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