Image retrieval with rotation invariance

The enormous increase in the image database sizes, as well as its vast development in various applications, retrieval of images based on its content is an important research area with application to digital libraries and multimedia databases. Content Based Image Retrieval (CBIR) is one of the method...

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Bibliographic Details
Published in2011 3rd International Conference on Electronics Computer Technology Vol. 2; pp. 194 - 198
Main Authors Sekhar, P. N. R. L. Chandra, Prasad, P. Surya, Kumar, M. Vinodh, Santosh, D. Hari Hara
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Feature extraction
feature vector
Image color analysis
Image retrieval
Image segmentation
Rotation Invariance
Shape
Similarity check
Texture features
Transforms
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Summary:The enormous increase in the image database sizes, as well as its vast development in various applications, retrieval of images based on its content is an important research area with application to digital libraries and multimedia databases. Content Based Image Retrieval (CBIR) is one of the methods which use low level features like color, texture and shape information to retrieve the images. Effective texture feature is an essential component in any content based image retrieval systems in which Curvelet captures the texture features more efficiently than other spectral features like Gabor and wavelet. This paper describes an approach to achieve a rotation invariant CBIR by using Curvelet texture feature and make them into different segments of similar groups to improve the efficiency of retrieval. Combine all these features into a feature vector and retrieve the similar images based on similarity check. This approach gives better results even for the images found in different orientations.
ISBN:1424486785
9781424486786
DOI:10.1109/ICECTECH.2011.5941683