Image Classification Based on the Fusion of Complementary Features  

作　　者：Huilin Gao Wenjie Chen 

机构地区：[1]School of Automation, Beijing Institute of Technology, Beijing 100081, China [2]State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing 100081, China

出　　处：《Journal of Beijing Institute of Technology》2017年第2期197-205,共9页北京理工大学学报（英文版）

基　　金：Supported by Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61321002);Projects of Major International(Regional)Jiont Research Program NSFC(61120106010);Beijing Education Committee Cooperation Building Foundation Project;Program for Changjiang Scholars and Innovative Research Team in University(IRT1208)

摘　　要：Image classification based on bag-of-words（BOW）has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent.To deal with this problem,this paper proposes to combine two ingredients：（i）Three features with functions of mutual complementation are adopted to describe the images,including pyramid histogram of words（PHOW）,pyramid histogram of color（PHOC）and pyramid histogram of orientated gradients（PHOG）.（ii）An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed.Experiments are carried out on the Caltech101 database,which confirms the validity of the proposed approach.The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14%higher than that of the traditional BOW methods.With full utilization of global,local and spatial information,the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition.Significant improvements to the classification accuracy are achieved as the result.Image classification based on bag-of-words（BOW）has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent.To deal with this problem,this paper proposes to combine two ingredients：（i）Three features with functions of mutual complementation are adopted to describe the images,including pyramid histogram of words（PHOW）,pyramid histogram of color（PHOC）and pyramid histogram of orientated gradients（PHOG）.（ii）An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed.Experiments are carried out on the Caltech101 database,which confirms the validity of the proposed approach.The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14%higher than that of the traditional BOW methods.With full utilization of global,local and spatial information,the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition.Significant improvements to the classification accuracy are achieved as the result.

关 键 词：image classification complementary features bag-of-words （BOW） feature fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]