Extraction of combined features from global/local statistics of visual words using relevant operations

Tetsu Matsukawa; Takio Kurita

doi:10.1587/transinf.E93.D.2870

Extraction of combined features from global/local statistics of visual words using relevant operations

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents a combined feature extraction method to improve the performance of bag-of-features image classification. We apply 10 relevant operations to global/local statistics of visual words. Because the pairwise combination of visual words is large, we apply feature selection, methods including fisher discriminant criterion and L1-SVM. The effectiveness of the proposed method is confirmed through the experiment.

Original language	English
Pages (from-to)	2870-2874
Number of pages	5
Journal	IEICE Transactions on Information and Systems
Volume	E93-D
Issue number	10
DOIs	https://doi.org/10.1587/transinf.E93.D.2870
Publication status	Published - Oct 2010
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

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10.1587/transinf.E93.D.2870

Cite this

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abstract = "This paper presents a combined feature extraction method to improve the performance of bag-of-features image classification. We apply 10 relevant operations to global/local statistics of visual words. Because the pairwise combination of visual words is large, we apply feature selection, methods including fisher discriminant criterion and L1-SVM. The effectiveness of the proposed method is confirmed through the experiment.",

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Extraction of combined features from global/local statistics of visual words using relevant operations

Abstract

All Science Journal Classification (ASJC) codes

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