Color-frequency-orientation histogram based image retrieval

This paper proposes a multiclass image retrieval method using combined color-frequency-orientation histogram. Shape information, obtained via edge detector and Hough Transform, is also incorporated into the new feature. The feature has shown advantage in both unsupervised and supervised learning on Corel image dataset containing 10 categories of 1000 complex scenes. In unsupervised learning, comparing with histogram-based method [1], SIMPLIcity [2], FIRM [3], edge-based method [4], multi-resolution-based method [5], our approach respectively shows 25%, 14%, 10%, 7% and 2% improvement in accuracy. In supervised learning, we implement both one-against-one SVM and one-against-all SVM for multiclass classification. One-against-all SVM beats one-against-one SVM, achieving 95% accuracy with sufficient training.