This paper introduces an effective method for query adaptive image retrieval using low-level feature extraction and hashing method. The low-level feature primarily constitute color, shape and texture features. For color feature extraction color moments, color Histogram and color correlogram method were implemented and for texture feature extraction used wavelet moment method. Hashing methods is used to embed high-dimensional image features into Hamming space, where search can be performed in real-time based on
Hamming distance of compact hash codes. Depending upon minimum Hamming distance it returns the similar image to query image.
Index Terms—Hash Code, Hamming distance, Hamming
Space.
I. INTRODUCTION
WEB data including documents, images and videos are increasing rapidly. Consider an example of website Flickr mainly used for photo sharing and it consist over 5 billion images. For finding relevant image from such massive databases we need some easy method. Moreover search engine like Google and Bing based on textual input; content-based image retrieval (CBIR) has attracted substantial attention over the past decade. Instead of taking textual keywords as input, CBIR techniques directly take a visual query image Q and try to return images which are closer to query image from a given database X using a prespecified feature space and distance measure.
Generally a large-scale image search system consists of two key components “an effective image feature representation and an efficient search mechanism”.
Basically the quality of image searching results relies heavily on the representation power of image features [1].
The efficient search mechanism is critical when existing image features are mostly of high dimensions and current image ...
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...th Hash
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[2] Jun Wang, Sanjiv Kumar and Shih-Fu Chang” Semi-
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[3] Chenxia Wu, Jianke Zhu, Deng Cai, Member, Chun Chen, and
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2009
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Middle Search Plus. Web. The Web. The Web. 1 Oct. 2015 -.