Unifying local and global content-based similarities for home photo retrieval
Unlike professional or domain-specific images, home photos vary significantly. They pose great challenge for content-based image retrieval. In this paper, we propose a Bayesian formulation to unify both local and global content-based similarities for image matching and demonstrate its superior retrieval performance on 2400 genuine home photos. Our proposed framework uses support vector machines to extract and combine intraimage and interclass semantics. Support vector detectors are first trained on semantically meaningful regions and used to form detection image indexes. The indexes then serve as input for support vector learning of image classifiers to generate class-relative indexes. During image retrieval, similarities based on both detection-based and class-relative indexes are combined to rank images. Query-by-example experiments on 2400 home photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed the fusion of color and texture features by 55% in average precision.