吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 943-951.doi: 10.13229/j.cnki.jdxbgxb20170444

• Orginal Article • Previous Articles     Next Articles

Image classification methods based on space vector model

CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng   

  1. College of Communication Engineering, Jilin University, Changchun 130022, China
  • Received:2017-05-04 Online:2018-05-20 Published:2018-05-20
  • Supported by:
     

Abstract: A space vector model is proposed to overcome the lack of spatial location information in the bag-of-words model. The model turns visual words into vector model using the space location information of visual words to represent image, thereby achieves better classification performance. Experiments are carried out on two standard image datasets Caltech-101 and Caltech-256, respectively, with Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The results show that the space vector model can effectively improve the Average Classification Accuracy (ACA) and Average Category Precision (ACP), and has a good classification effect.

Key words: information processing technology, image classification, space vector model, bag-of-words model, vector matrix

CLC Number: 

  • TP391.4
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