Spatial Color Indexing: An Efficient and Robust Technique for Content-Based Image Retrieval

Abstract

Problem statement: Color Histogram is admitted as a useful representation of features because it is a statistical result and possesses the merits of simplicity, robustness and efficiency. However, the main problem with color histogram indexing is that it doesn't take into account the spatial information. Previous researches have proved that the effectiveness of image retrieval increases when spatial feature of colors is included in image retrieval. Approach: This study examined the use of a computational geometry-based spatial color indexing methodology, there are two major contributions: (1) Color Spatial Entropy (CSE) which introduce entropy to describe the spatial information of colors. (2) Color Hybrid Entropy (CHE) witch introduce a description spatial on multiresolution images. Results: The experiment results showed that CSE and CHE is more better performance and efficiently and relevant result than those traditional CBIR method based on the local histograms. Conclusion: our new system was presented to strengthen the retrieval efficacy and remains more stable performance by transformations geometry in more CHE characterize quantitatively the compactness of the multiresolution images.