Codebook Learning for Image Recognition Based on Parallel Key SIFT Analysis

Feng YANG  Zheng MA  Mei XIE  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.4   pp.927-930
Publication Date: 2017/04/01
Publicized: 2017/01/10
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8167
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
codebook learning,  image classification,  parallel key SIFT analysis,  ScSPM,  

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The quality of codebook is very important in visual image classification. In order to boost the classification performance, a scheme of codebook generation for scene image recognition based on parallel key SIFT analysis (PKSA) is presented in this paper. The method iteratively applies classical k-means clustering algorithm and similarity analysis to evaluate key SIFT descriptors (KSDs) from the input images, and generates the codebook by a relaxed k-means algorithm according to the set of KSDs. With the purpose of evaluating the performance of the PKSA scheme, the image feature vector is calculated by sparse code with Spatial Pyramid Matching (ScSPM) after the codebook is constructed. The PKSA-based ScSPM method is tested and compared on three public scene image datasets. The experimental results show the proposed scheme of PKSA can significantly save computational time and enhance categorization rate.