A Bayesian approach integrating regional and global features for image semantic learning

• Main Authors: NGUYEN, Luong-Dong, YAP, Ghim-Eng, LIU, Ying, TAN, Ah-hwee, CHIA, Liang-Tien, LIM, Joo-Hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6250; https://ink.library.smu.edu.sg/context/sis_research/article/7253/viewcontent/Image_Semantic_Learning_ICME_09.pdf
• Institution: Singapore Management University

In content-based image retrieval, the “semantic gap” between visual image features and user semantics makes it hard to predict abstract image categories from low-level features. We present a hybrid system that integrates global features (Gfeatures) and region features (R-features) for predicting image semantics. As an intermediary between image features and categories, we introduce the notion of mid-level concepts, which enables us to predict an image’s category in three steps. First, a G-prediction system uses G-features to predict the probability of each category for an image. Simultaneously, a R-prediction system analyzes R-features to identify the probabilities of mid-level concepts in that image. Finally, our hybrid H-prediction system based on a Bayesian network reconciles the predictions from both R-prediction and G-prediction to produce the final classifications. Results of experimental validations show that this hybrid system outperforms both Gprediction and R-prediction significantly