A Content-Based Painting Image Retrieval System Based on AdaBoost Algorithm

A content-based painting image retrieval (CBPIR) system based on AdaBoost is proposed. By providing query examples which share the same semantic concepts, e.g., portraits, and incorporating with relevance feedback (RF), the user can acquire the desired painting images. To bridge the gap between low-level features and semantic concepts, a large set of 4,356 features on texture and spatial arrangement of painting images is provided. Utilize the nice characteristic of AdaBoost algorithm that it can combine partial weak classifiers, i.e. features, into a strong one, the system can correctly discover a few most critical features from provided samples and search paintings sharing same features from the database. Our experiment in query of "portrait," based on 3 RFs and an average of 50 repetitions, shows an excellent performance of (approximately) 0.71, 0.84, 0.95 in Precision, Recall, and Top 100 Precision rates. The average execution time, based on 50 repetitions, required in initial query and three RF with training and classifying is approximately 1.2 seconds, thus a complete query takes less than 5 seconds in training and classifying. The system is proved to be accurate in content based image retrieval and also very efficient for on-line users.