Abstract
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 codevec- tor 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 pro- duces 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.
Highlights
The image capturing devices have increased the number of images that are stored in the digital formats to such an extent that retrieval of the images has become a big problem
To determine the image similarity based on encoding distortion distance (EDD)[13], codebooks of images are directly compared with the images as
Encoding Distortion (ED) can be used if the precision is to be higher and Histogram Intersection (HI) if the recall is to be higher
Summary
The image capturing devices have increased the number of images that are stored in the digital formats to such an extent that retrieval of the images has become a big problem. To facilitate the retrieval process, an index[2] of the image database is constructed that contains the similarity measure of the image to other images in the database. If name is not clearly assigned it cannot reflect on the content of image Another method is to assign tags for an image based on the textual content[1] close to it in the document. Vector quantization (VQ)[4] is an efficient technique for low bit rate image compression It is an indexing technique where the image is represented as a set of code vectors. VQ represents the image as a codebook that reflects the intensity and spatial content of the image. The distortion with the codebook or the intersection distance of the histogram is taken as a single feature to represent the image in the image index model
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