A decision tree approach for scene pattern recognition and extraction in snooker videos

Video content processing and analysis is going through a transition from low-level feature based techniques to high-level semantics based approaches. In this paper, we describe such a transition that low level features are processed to extract four semantic patterns, leading to high-level content analysis for snooker videos. Such extracted semantics and recognised patterns include: (i) full court scenes for snooker match, (ii) close-up view of snooker match; (iii) player’s face, and (iv) audience’s faces. Experimental results support that the proposed technique works well with snooker videos, providing a significant potential for automatic snooker video processing such as annotation, summarization and editing

Shot boundary detection based on Eigen coefficients and small Eigen value

Detection of shot boundaries in a video has been an active for quite a long time, till the TRECVID community almost declared it as a solved problem. A problem is assumed to be solved when there is no significant improvement being achieved from that of the state-of-the art methodologies. However, certain aspects can still be researched and improved. For instance, finding appropriate parameters instead of empirical thresholds to detect the shot boundaries is very challenging and is still being researched. In this paper, we present a fast, adaptive and non-parametric approach for detecting shot boundaries. Appearance based model is used to compute the difference between two subsequent frames. These frame distances, are then used to locate the shot boundaries. The proposed shot boundary detection algorithm uses an asymmetric region of support that automatically adapts to the shot boundaries. Experiments have been conducted to verify the effectiveness and applicability of the proposed method for adaptive shot segmentation