Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Skin Color Region Detection in MPEG Video Sequences
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Content-Based Retrieval of Video Data Based on Spatiotemporal Correlation of Objects
ICMCS '98 Proceedings of the IEEE International Conference on Multimedia Computing and Systems
Finding Edges and Lines in Images
Finding Edges and Lines in Images
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
Content-aware video adaptation under low-bitrate constraint
EURASIP Journal on Advances in Signal Processing
Multimedia Tools and Applications
Optical flow based dc surface compensation for artifacts reduction
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Fingerprint enhancement based on discrete cosine transform
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The semantic gap or semantic chasm is a well-known problem in content-based image and video retrieval. To address this problem, many techniques have been proposed in the literature. A more common approach is the use of low-level features such as colour, texture and shape for semantic analysis. Our focus in this paper is on the edge feature, which has not been exploited to the same extent as other low-level features for semantic analysis. In this paper, we present an algorithm for edge detection, and illustrate the usage of edges for semantic analysis of video content.We first propose an algorithm for detecting edges within video frames directly on the MPEG format without a decompression process. The algorithm is based on a spatial-domain synthetic edge model, which is defined using interrelationship of two DCT edge features: horizontal and vertical. We use a multistep approach to classify video sequences into meaningful semantic segments such as "goal", "foul", and "crowd" in basketball games using the "edgeness" criteria. We then show how an audio feature ("whistle") can be used as a filter to enhance edge-based semantic classification fro sports videos.