Combining SIFT and Invariant Color Histogram in HSV Space for Deformation and Viewpoint Invariant Image Retrieval

被引：0

作者：

Suhasini, P. S. ^{[1
]}

Krishna, K. Sri Rama ^{[2
]}

Krishna, I. V. Murali ^{[3
]}

机构：

[1] DMS SVH Coll Engn, Machilipatnam, India

[2] V R Siddartha Engn Coll, Vijayawada, Andhra Pradesh, India

[3] JNT Univ, Hyderabad, Andhra Pradesh, India

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC) | 2012年

关键词：

SIFT; Invariant Color Histogram; Deformation and Viewpoint Invariance; HSV color Space;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel approach to retrieve images which are taken at different viewpoints, using combined feature descriptors. The content of the image is extracted with two descriptors, Scale Invariant Feature Transform (SIFT) and Deformation and view point Invariant Color Histogram (ICH) in HSV color space. SIFT has been proven to be the most reliable descriptor for rotation, translation and partially to illumination and affine or 3D projection invariant image matching. However, it is designed for gray images. Invariant Histogram is developed for creating color Histogram based on color gradients which are invariant to deformation and changes in viewpoint and is developed in RGB color space. To increase the deformation and viewpoint invariance capability and thus to improve image recognition, SIFT features are combined with ICH in HSV color space for Image Retrieval. Experimental results show that robust retrieval can be achieved even for seriously occluded images.

引用

页码：666 / 669

页数：4

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