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Improved object recognition results using SIFT and ORB feature detector

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Abstract

Object recognition has a wide domain of applications such as content-based image classification, video data mining, video surveillance and more. Object recognition accuracy has been a significant concern. Although deep learning had automated the feature extraction but hand crafted features continue to deliver consistent performance. This paper aims at efficient object recognition using hand crafted features based on Oriented Fast & Rotated BRIEF (Binary Robust Independent Elementary Features) and Scale Invariant Feature Transform features. Scale Invariant Feature Transform (SIFT) are particularly useful for analysis of images in light of different orientation and scale. Locality Preserving Projection (LPP) dimensionality reduction algorithm is explored to reduce the dimensions of obtained image feature vector. The execution of the proposed work is tested by using k-NN, decision tree and random forest classifiers. A dataset of 8000 samples of 100-class objects has been considered for experimental work. A precision rate of 69.8% and 76.9% has been achieved using ORB and SIFT feature descriptors, respectively. A combination of ORB and SIFT feature descriptors is also considered for experimental work. The integrated technique achieved an improved precision rate of 85.6% for the same.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India

    Surbhi Gupta

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  3. Department of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India

    Anupam Garg

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  1. Surbhi Gupta
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  2. Munish Kumar
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  3. Anupam Garg
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Correspondence to Munish Kumar.

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Gupta, S., Kumar, M. & Garg, A. Improved object recognition results using SIFT and ORB feature detector. Multimed Tools Appl 78, 34157–34171 (2019). https://doi.org/10.1007/s11042-019-08232-6

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  • DOI: https://doi.org/10.1007/s11042-019-08232-6

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