Probabilistic Region Relevance Learning for Content-Based Image Retrieval

Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retrieval metrics for producing neighborhoods that are elongated along less relevant feature dimensions and constricted along most influential ones. Based on the observation that regions in an image have unequal importance for computing image similarity, we propose a probabilistic method inspired by PFRL, probabilistic region relevance learning (PRRL), for automatically estimating region relevance based on user's feedback. PRRL can be used to set region weights in region-based image retrieval frameworks that use an overall image-to-image similarity measure. Experimental results on general-purpose images show the effectiveness of PRRL in learning the relative importance of regions in an image.

• Type: Conference
• Year: 2004
• Where: CISST
• Authors: Iker Gondra, Douglas R. Heisterkamp
• Keywords: CISST 2004, Feature Relevance, Hardware, Image Retrieval, Probabilistic Feature Relevance