Content based image retrieval by IPP algorithm

In order to realize the content-based image retrieval (CBIR), some characteristics of the images should be extracted like color, texture and shape. The extremely important thing in CBIR is to search the most similar database images to match the query image, which needs to improve the precision. This paper proposes an Improving Precision Priority (IPP) algorithm integrating vital features and the query method to improve performance. Proposed IPP algorithm has two phases. In the first phase, both of the query image and database images are divided into several blocks respectively. After that, we calculate the color histogram of each block. Then we take Euclidean distance to compare the similarities to complete the first round of retrieval. To calculate the distance, we allocate different blocks to different weights, the blocks of the central part always containing much useful information should be allocated more weight. And the surrounding part are allocated less and the corners have the smallest weight. All of the distances of the small blocks are accumulated together to be the distance of the whole image. In this phase we can retrieve some related images from the database denoting as result A. In the second phase, shape characteristics of result A are extracted using Hu moment invariants. After that, we calculate the invariant moments similarities between the query image and those of result A images. The most similar images are shown as the final result. IPP algorithm can increase the precision.