Title: Fusing multiple features and spatial information for image classification via codebook ensemble

Authors: Huilan Luo; Chengtao Wan; Minjie Guo

Addresses: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China

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.

Keywords: image classification; feature fusion; codebook ensemble.

DOI: 10.1504/IJES.2017.084691

International Journal of Embedded Systems, 2017 Vol.9 No.3, pp.229 - 240

Received: 18 Nov 2015
Accepted: 05 Apr 2016
Published online: 21 Jun 2017 *

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