Multimedia Tools and Applications

, Volume 78, Issue 23, pp 34157–34171 | Cite as

Improved object recognition results using SIFT and ORB feature detector

  • Surbhi Gupta
  • Munish KumarEmail author
  • Anupam Garg


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.


Object Recognition ORB SIFT K-Means LPP k-NN Decision Tree Random Forest 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringGokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia
  2. 2.Department of Computational SciencesMaharaja Ranjit Singh Punjab Technical UniversityBathindaIndia
  3. 3.Department of Computer Science & EngineeringThapar Institute of Engineering & TechnologyPatialaIndia

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