A Study on the Effect of Codebook and CodeVector Size on Image Retrieval Using Vector Quantization

In this paper, we study the effect of codebook size and codevector size using vector quantization (VQ) for retrieval of images, not restricted to the compressed domain. We use the image index model, to study the precision and recall values for different similarity measures. The study presents the following findings. The codebook size and codevector size are directly proportional to the precision value for a locally global codebook, but are dependent on the size of the source image for a local codebook. The Encoding Distortion similarity measure calculated from the local codebook produces the highest precision for the same recall over all other similarity measures. The Histogram Intersection using locally global codebook gives higher precision for higher codebook sizes. It is established that VQ can be used to create a single valued feature to represent the image in the image index model. This feature based on distortion measure can be effectively used for image retrieval based on the experimental results.