Image searching, is an active approach to recover the effective results for image searched by the users with the help of queries. Which is used by the current required search engines likes Bing, Google, and Internet Explorer and so on. To improve image searching method there is use of hash code technique. In this paper, various image search techniques using different hashing methods are reviewed. More than a few hashing methods such as state of the art which is used to generate hash codes, then embed and extract features of images in the high-dimensional practice. This scale image search can be executed in real time; this is depends on Hamming distance. This technique contains a weighted Hamming distance and finer-grained ranking. Query adaptive weights consist of semantic concept classes which improves the result of an image search. With the Query adaptive bit weights, images are ranked and calculated by weighted Hamming distance.

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Query-adaptive Image Search Scalability Hash Codes Weighted Hamming Distance Query-adaptive Ranking Binary Code Image Search.