A Bayesian approach integrating regional and global features for image semantic learning
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 ima...
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| Main Authors: | , , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2009
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 |
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