A Performance-Driven Rotational Invariant Image Retrieval System

The color feature could be well represented in a feature and shape is a distinguishable feature which can make coarse discriminations quickly. Based on those reasons, we present an efficient image retrieval system to consider simultaneously accuracy and performance by combining color moment with shape contexts in this paper. The shape rotation has significant influence on the similarity measure and retrieval performance. Another important contribution of the paper is to present an efficient algorithm for rotation invariance. In the algorithm, the rotated shape contexts together are first clustered and then label each cluster so that the shape contexts in each cluster have the same label. Using the histogram of label frequencies can quickly and efficiently search for similar or rotational shapes. The experimental results have shown that our system is effective and has better retrieval performance than the existing systems.