Dimensionality Reduction of SIFT Descriptor Using Vector Decomposition for Image Classification

  • Dhirendra KumarEmail author
  • Ramesh Kumar Agrawal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 614)


Image classification involves extraction of repeatable and robust feature points from images for classification. These extracted feature points are described in terms of feature vectors. Scale Invariant Feature Transform (SIFT) is one of the popular feature descriptor for obtaining the feature vectors from images for image classification, image retrieval and object recognition. However, the dimension of feature vector obtained using SIFT is high. Hence, the computation time to build decision model using SIFT for image classification, image retrieval and object recognition is high and also requires large memory. In this work, we have proposed SIFT-64 and SIFT-32 to reduce the dimension of the conventional SIFT for image classification. To check the efficacy of the proposed SIFT-64 and SIFT-32, experiments were performed on two well-known publicly available datasets namely, CalTech6 and Graz. The performance is evaluated in terms of classification accuracy, training and testing time. Experimental results demonstrate superior performance of the SIFT-64 and SIFT-32 in comparison to conventional SIFT in terms of both training and testing time without compromising much on classification accuracy. The proposed feature descriptor outperforms the PCA-SIFT descriptor in terms of classification accuracy, training and testing time.


Image classification Detector Feature descriptor SIFT PCA 



The authors express their gratitude to the Council of Scientific & Industrial Research (CSIR), India, for the obtained financial support in performing this research work.


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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