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Supervised statistical learning has become a critical means to design and learn visual concepts (e.g., faces, foliage, buildings, etc.) in content-based indexing systems. The drawback of this approach is the need of manual labeling of regions. While several automatic image annotation methods proposed recently are very promising, they usually rely on the availability and analysis of associated text descriptions. In this paper, we propose a hybrid learning framework to discover local semantic regions and generate their samples for training of local detectors with minimal human intervention. A multiscale segmentation-free framework is proposed to embed the soft presence of discovered semantic regions and local class patterns in an image independently for indexing and matching. Based on 2400 heterogeneous consumer images with 16 semantic queries, both similarity matching based on individual index and integrated similarity matching have outperformed a feature fusion approach by 26% and 37% in average precisions, respectively.

Discovering Recurrent Image Semantics from Class Discrimination