Interactive Content-Based Retrieval in Video Databases Using Fuzzy Classification and Relevance Feedback

This paper presents an integrated framework for interactive content-based retrieval in video databases by means of visual queries. The proposed system incorporates algorithms for video shot detection, keyframe and shot selection, automated video object segmentation and tracking, and construction of multidimensional feature vectors using fuzzy classification of color, motion or texture segment properties. Retrieval is then performed in an interactive way by employing a parametric distance between feature vectors and updating distance parameters according to user requirements using relevance feedback. Experimental results demonstrate increased performance and flexibility according to user information needs.

• Type: Conference
• Year: 1999
• Where: ICMCS
• Authors: Anastasios D. Doulamis, Yannis S. Avrithis, Nikolaos D. Doulamis, Stefanos D. Kollias
• Keywords: Feature Vectors, ICMCS 1999, Interactive Content-based Retrieval, Multidimensional Feature Vectors, Multimedia