Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University››2012,Vol. 46››Issue (09): 1406-1410.

• Automation Technique, Computer Technology •Previous ArticlesNext Articles

Image Categorization Algorithm Based on Improved Sparse Coding Model

TANG Feng-1, SUN Tan-Feng-1, 2 , JIANG Xing-Hao-1, 2 , LU Huan-1

  1. (1. School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. Shanghai Information Security Management and Technology Research Key Laboratory, Shanghai 200240, China)
  • Received:2011-10-14Online:2012-09-28Published:2012-09-28

Abstract:A novel image categorization algorithm based on improved sparse coding model for image representation and Random Forests for image classification was proposed. Firstly SIFT (ScaleInvariant Feature Transform) descriptors were extracted from images. Then sparse coding was adopted to train a visual dictionary and convert SIFT descriptors into sparse vectors. An efficient pooling method was employed to merge the sparse vectors in each grid of image and a pooled sparse vector was formed to represent the grid. According to the position of grids, the pooled sparse vectors were combined to form a single sparse vector for representing an image. Secondly Random Forests, a multiclass classifier was employed to classify sparse vectors which represent images. The experimental results show that the algorithm outperforms several stateofthearts in image categorization.

Key words:image categorization,sparse coding,random forests,visual word,BoW model

CLC Number:

Baidu
map