Coding and Indexing Shape Feature using Golomb-Rice Coding for CBIR Applications

The Content-Based Image Retrieval application uses large amount of low-level features of images for retrieving relevant images. Numerous low-level features are used for retrieval and among them the shape based feature is important and can be represented in the form of histogram. It is well known that the low-level feature requires more storage space and thus entire content has to be coded, indexed etc. to reduce the storage requirement, feature processing time and accessing time. In this work, indexing and encoding mechanism is proposed to effectively access the feature and represent the feature in lesser space. The bin values are used as reference for reducing the dimension of the histogram. The non-zero values are coded using Golomb-Rice coding and represented as compact histogram. The size of the compact histogram is variable in size and thus a similarity measure is proposed to handle the issue. Various well-known benchmark datasets are used for experiments to evaluate the performance. It is found that the truncated and encoded histogram performs well and achieves high precision of retrieval.