BoVW Model for Animal Recognition: An Evaluation on SIFT Feature Strategies

  • Leila Mansourian
  • Muhamad Taufik AbdullahEmail author
  • Lilli Nurliyana Abdullah
  • Azreen Azman
  • Mas Rina Mustaffa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)


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.


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) 



This article was kindly supported by Ministry of Higher Education under Fundamental Research Grant Scheme (FRGS).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leila Mansourian
    • 1
  • Muhamad Taufik Abdullah
    • 1
    Email author
  • Lilli Nurliyana Abdullah
    • 1
  • Azreen Azman
    • 1
  • Mas Rina Mustaffa
    • 1
  1. 1.Department of Multimedia, Faculty of Computer Science and Information TechnologyUniversity Putra Malaysia (UPM)SerdangMalaysia

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