Image Indexing and Retrieval
Keywords:
hash codes, high-dimensional image features, scalability, bitwise weights, weighted Hamming distanceAbstract
Scalable content based image search based on hash codes is hot topic nowadays. The existing hashing methods have a drawback of providing a fixed set of semantic preserving hash functions to the labelled data for the images. However, it may ignore the user’s search intention conveyed through the query image. Again these hashing methods embed high -dimensional image features into hamming space performing real time search based on hamming distance. This paper introduces a n approach that generates the most appropriate binary codes for different queries. This is done by firstly offline generating bitwise weights of the hash codes for a set of predefined semantic classes. At query time, query adaptive weights are computed online by finding out the proximity between a query and the semantic concept classes. Then these images can be ranked by weighted Hamming distance at a finer-grained hash code level rather than the original Hamming distance level.
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