Retrieval Based on Indexing for Compressed Domain

Abstract

An .image indexing method is presented to perform the CBIR(Content Based Image Retrieval) for about 100 different images. The method is based on the fractal framework of the PIFS(Partitioned iterated function systems) widely used for image compression. The image indexing is represented through i-model features. A specific searching algorithm allows to use this representation as an indexing to retrieval an images. To optimize the matching and retrieval process, an indexed data structure is used to store the PIFS and to calculate the distance between the compressed images and the index image. Also, we present the theoretical arguments to justify their use in image retrieval.

The application of fractal compression to content-based image retrieval: Comparison of methods

“…(3) Here we use three algorithms from the articles [6][9] [10] and test their image-retrieval efficiency on image database. In addition, we will compare technical detail of their function to see whether it will make any difference or not in image-retrieval.…”

“…[10] In order to shorten the time of selection, they organized the fractal code into several index and used those index to search the data. They found that this could accelerate the period of image retrieval.…”

A modified algorithm of image retrieval by using modified PIFS coding method

“…He used Fisher's discrimination function to judge whether the selected image is similar to the one wanted or not. [6].…”

The Application of Fractal Concept to Content-Based Image Retrieval

“…He used Fisher's discrimination function to judge whether the selected image is similar to the one wanted or not. [7] Then in 2008, Fan et al suggested to use fractal code to select images. In order to shorten the time of selection, they organized the fractal code into several index and used those index to search the data.…”