Fusion of local and global features for effective image extraction

Fusion of local and global features for effective image extraction Image extraction methods rely on locating interest points and describing feature vectors for these key points. These interest points provide different levels of invariance to the descriptors. The image signature can be described well by the pixel regions that surround the interest points at the local and global levels. This contribution presents a feature descriptor that combines the benefits of local interest point detection with the feature extraction strengths of a fine-tuned sliding window in combination with texture pattern analysis. This process is accomplished with an improved Moravec method using the covariance matrix of the local directional derivatives. These directional derivatives are compared with a scoring factor to identify which features are corners, edges or noise. Located interest point candidates are fetched for the sliding window algorithm to extract robust features. These locally-pointed global features are combined with monotonic invariant uniform local binary patterns that are extracted a priory as part of the proposed method. Extensive experiments and comparisons are conducted on the benchmark ImageNet, Caltech-101, Caltech-256 and Corel-100 datasets and compared with sophisticated methods and state-of-the-art descriptors. The proposed method outperforms the other methods with most of the descriptors and many image categories. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Fusion of local and global features for effective image extraction

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0916-1
Publisher site
See Article on Publisher Site

Abstract

Image extraction methods rely on locating interest points and describing feature vectors for these key points. These interest points provide different levels of invariance to the descriptors. The image signature can be described well by the pixel regions that surround the interest points at the local and global levels. This contribution presents a feature descriptor that combines the benefits of local interest point detection with the feature extraction strengths of a fine-tuned sliding window in combination with texture pattern analysis. This process is accomplished with an improved Moravec method using the covariance matrix of the local directional derivatives. These directional derivatives are compared with a scoring factor to identify which features are corners, edges or noise. Located interest point candidates are fetched for the sliding window algorithm to extract robust features. These locally-pointed global features are combined with monotonic invariant uniform local binary patterns that are extracted a priory as part of the proposed method. Extensive experiments and comparisons are conducted on the benchmark ImageNet, Caltech-101, Caltech-256 and Corel-100 datasets and compared with sophisticated methods and state-of-the-art descriptors. The proposed method outperforms the other methods with most of the descriptors and many image categories.

Journal

Applied IntelligenceSpringer Journals

Published: Apr 11, 2017

References

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