Content-Based Retrieval of News Videos Using Relevance Feedback

Abstract

Content-based retrieval in news video databases has become an important task with the availability of large quantities of data in both public and proprietary archives. We describe a relevance feedback technique that captures the significance of different features at different spatial locations in an image. Spatial content is modeled by partitioning images into non-overlapping grid cells. Contributions of different features at different locations are modeled using weights defined for each feature in each grid cell. These weights are iteratively updated based on user's feedback in terms of positive and negative labeling of retrieval results. Given this labeling, the weight updating scheme uses the ratios of standard deviations of the distances between relevant and irrelevant images to the standard deviations of the distances between relevant images. The proposed technique is quantitatively and qualitatively evaluated using shots related to several sports from the news video collection of the TRECVID video retrieval evaluation where the weights could capture relative contributions of different features and spatial locations