Image Retrieval for Highly Similar Objects

In content-based image retrieval, precision is usually regarded as the top metric used for performance measurement. With image databases reaching hundreds of millions of records, it is apparent that many retrieval strategies will not scale. Data representation and organization has to be better understood. This paper focuses on: (a) feature selection and optimal representation of features and (b) multidimensional tree indexing structure. The paper proposes the use of a forward and conditional backward searching feature selection algorithm. The data is then put through a minimum description length based optimal non-uniform bit allocation algorithm to reduce the size of the stored data, while preserving the structure of the data. The results of our experiments show that the proposed feature selection process with a minimum description length based non-uniform bit allocation method gives a system that improves retrieval time and precision.