Rotation, Translation, and Scale Invariant Bag of Feature Based on Feature Density

In this paper, we propose a feature representation that achieves translation, rotation, and scale invariant simultaneously. We first proposed a novel component, called Block Based Integral Image, to search the densest region of feature points. This aims to find the center of potential object in the...

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Bibliographic Details
Published inProceedings (International Conference on Intelligent Systems, Modelling and Simulation.) pp. 163 - 168
Main Authors Shih-Min Chen, Chen-Kuo Chiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2016
Subjects
Computational modeling
Computer vision
Feature Density
Feature extraction
Histograms
Image recognition
Image Representation
Indexes
Robustness
Rotation Translation
Scale Invariance
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Summary:In this paper, we propose a feature representation that achieves translation, rotation, and scale invariant simultaneously. We first proposed a novel component, called Block Based Integral Image, to search the densest region of feature points. This aims to find the center of potential object in the image. Then, with the improved object center, we apply Spatial Pyramid Ring (SPR) by to handle translation and rotation invariant representation. After that, histogram equalization technique is utilized to adjust representation for scale invariant. The experimental results are demonstrated on different datasets by image classification task. Experimental results show that our translation, rotation, and scale invariant representation achieves higher accuracy than the previous methods.
ISSN:2166-0670
DOI:10.1109/ISMS.2016.12