Cluster Indexing and GR Encoding with Similarity Measure for CBIR Applications



In content-based image retrieval applications, there is an exhaustive search in the image database for finding relevant images, which is non-scalable. This chapter presents methods on indexing scheme, encoding scheme and similarity measure for handling the non-scalable issue. An image is represented in terms of colour feature, and the bin content of the feature is analysed to understand the colour content of the images. Based on the bin values and its contribution to the colour information, the size of the feature is truncated. The features are clustered based on the dimension of the histogram. The bin values of the truncated feature are encoded with Golomb–Rice (GR) coding scheme. The similarity between the query and database image is calculated by measuring the degree of overlap in terms of bins and its content. Benchmark datasets are used for evaluating the performance of the all the proposed schemes.


Cluster indexing GR encoding Similarity measure BOSM Common bin 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDayananda Sagar UniversityBangaloreIndia
  2. 2.Department of Computer Science and EngineeringSRM University APAmaravatiIndia

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