Freight Status Classification in Real-World Images Using SIFT and KNN Model

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


This paper proposes a unified image classification framework to label railway freights status that includes the Scale-Invariant Feature Transform (SIFT) description through a robust optimization approach. The developed model consists of several computational stages: (a) the SIFT descriptors in each image are extracted; (b) the training features are optimized by using K-Affinity Propagation (K-AP) algorithm; (c) construction of the Expectation-Maximization Principal Component Analysis (EMPCA) is applied for feature compression into low dimensional space; and finally (d) k-nearest neighbor (KNN) is used to register each image to trained classifiers. In this paper we are particularly interested to evaluate the classification performance of proposed algorithm on a diverse dataset of 600 real-world freights images. The experimental results show the effectiveness of proposed feature optimization technique when compared with the performance offered by the same classification schema with different feature descriptors.


Image classification Freights status classification SIFT K-AP EMPCA KNN 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dongyang Wang
    • 1
  • Dahai Yu
    • 1
    • 2
  • Junwei Han
    • 1
  • Shujun Li
    • 2
  1. 1.School of AutomationNorthwestern Poly-technical UniversityXi’anChina
  2. 2.Tianjin Optical Electrical GaoSi Communication Engineering Technology Co., LtdTianjinChina

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