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BoVW model for animal recognition: an evaluation on SIFT feature strategies


Mansourian, Leila and Abdullah, Muhamad Taufik and Abdullah, Lili Nurliyana and Azman, Azreen and Mustaffa, Mas Rina (2015) BoVW model for animal recognition: an evaluation on SIFT feature strategies. In: Advances in Visual Informatics: 4th International Visual Informatics Conference, IVIC 2015, Bangi, Malaysia, November 17-19, 2015, Proceedings. Lecture Notes in Computer Science (9429). Springer International Publishing, Switzerland, pp. 227-236. ISBN 9783319259383; EISBN: 9783319259390

Abstract / Synopsis

Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results.

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Additional Metadata

Item Type: Book Section
Divisions: Faculty of Computer Science and Information Technology
DOI Number:
Publisher: Springer International Publishing
Keywords: Bag of visual words; Content-based image retrieval (CBIR); Feature quantization; Image Classification; Scale invariant feature transform (SIFT) feature; Support vector machines (SVM); Dense scale invariant feature transform (DSIFT); Multi-Scale dense scale invariant feature transform (MSDSIFT)
Depositing User: Azhar Abdul Rahman
Date Deposited: 27 Jun 2016 14:01
Last Modified: 27 Jun 2016 14:01
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