Fusion framework for effective color image retrieval

• Useful modifications are suggested to original CDH algorithm.
• Image retrieval results of modified CDH and ART are fused.
• Fusion improves average retrieval accuracy by 16% over original CDH algorithm.
• Fusion improves average retrieval accuracy by 14% over global ART algorithm.
• Experiments on various standard databases prove the efficacy of this framework.

This paper presents a novel framework for color image retrieval through combination of all the low level features, which gives higher retrieval accuracy. The Color Difference Histogram (CDH) and Angular Radial Transform (ART) features are exploited to capture color, texture and shape information of an image. The CDH algorithm is modified in order to make the proposed system more effective. The proposed fusion framework combines the ranking results of the aforementioned descriptors through various post-classification methods i.e. Borda Count method, Min–max and Z-score normalization. The maximum retrieval accuracy attained in terms of average precision using Min–max normalization on Wang’s database is 78.3% when ART is applied on non-overlapping regions of the images. The proposed fusion framework is recommended because it improves the average retrieval accuracy by approximately 16% and 14% over CDH and ART respectively. Extensive experiments are carried out on different databases to establish the efficacy of the proposed scheme.