Fusing multiple features and spatial information for image classification via codebook ensemble. (2017)

• Record Type: Journal Article
• Authors: Luo, Huilan; Wan, Chengtao; Guo, Minjie
• Is Part Of: International journal of embedded systems. Volume 9:Number 3(2017)
• Page Start: 229
• Page End: 240
• Publication Date: 2017
• Subjects: image classification -- feature fusion -- codebook ensemble; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

The construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminate properties. This paper presents a discriminative spatial codebook ensemble learning approach for image classification with three key innovations: 1) images are first divided into sub-regions according to a spatial pyramid, and then initial big member spatial codebooks are constructed by grouping features of sub-regions into a number of clusters, one member spatial codebook for one sub-region; 2) the discriminative member spatial codebook is formed by selecting the visual words with higher probability of occurring in the images. Then the features of each sub-region are coded by LLC based on its corresponding member codebook; 3) combining SIFT and KDES-G features to describe images is also proposed by generating a joint vector as a new feature vector. The experimental results on the Caltech101 and 15 scenes datasets have shown that the proposed method has better performance and robustness compared with some state-of-the-art works.