Selective hidden random fields: Exploiting domain-specific saliency for event classification

Classifying an event captured in an image is useful for understanding the contents of the image. The captured event provides context to refine models for the presence and appearance of various entities, such as people and objects, in the captured scene. Such contextual processing facilie generation of better abstractions and annotations for the image. Consider a typical set of consumer images with sports-related content. These images are taken mostly by amateur photographers, and often at a distance. In the absence of manual annotation or other sources of information such as time and location, typical recognition tasks are formidable on these images. Identifying the sporting event in these images provides a context for further recognition and annotation tasks. We propose to use the domainspecific saliency of the appearances of the playing surfaces, and ignore the noninformative parts of the image such as crowd regions, to discriminate among different sports. To this end, we present a ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Vidit Jain, Amit Singhal, Jiebo Luo
• Keywords: Captured Event, Computer Vision, Consumer Images, CVPR 2008, Selection Criteria Corresponds, Sports-related Images, Typical Recognition Tasks