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Time-shift is a crucial function of interactive Televisions as like DVR and internet TV broadcasting services. Automatic important event detection allows users utilize time- shift function conveniently. In this paper, we propose a method to extract important events in baseball videos. In the proposed method, we first detect play scenes and audio events separately from video and audio tracks. For robust play scene extraction, we proposed off-line learning model having local adaptation based on ongoing analyzed video. And we implemented the audio event detection with a SVM-based classifier. Final important events are determined by a combination of each audio-visual detection results in real time. We evaluated our method with a baseball database of Korean and Major League games. Experimental results show that the implemented system runs in real time and achieves a remarkable performance of 0.85 recall and 0.97 precision rates. [ABSTRACT FROM AUTHOR]

This paper presents a video retrieval system (VRS) for Interactive-Television as like internet protocol television (IPTV). A video bookmark initiated by users is performed based on snippets of the background soundtrack corresponding to the ongoing program. Our VRS has two special aspects compared with previous bookmark systems. First, we adopt the robust audio fingerprint feature of long-term logarithmic modified DCT modulation coefficients (LMDCT-MC) for audio indexing and retrieval. Second, we propose and apply a two-stage search (TSS) algorithm for fast searching. In the first stage of TSS, candidate video segments are roughly determined with audio index bit vectors (IBV) and then the optimal video clip is obtained by fingerprint bit vectors (FBV). We evaluate the proposed system with a database of 100 TV programs including news, panel discussions, music shows, advertisements, and dramas. The experimental results show that our VRS achieve fast search, robustness to noise and high precision of retrieval. A search accuracy of 99.67% was accomplished. [ABSTRACT FROM AUTHOR]

In this paper, an integrated music recommendation system is proposed, which contains the Junctions of automatic music genre classification, automatic music emotion classification, and music similarity query. A novel tempo feature, named as log-scale modulation frequency coefficients, is presented in this paper. With AdaBoost algorithm, the proposed tempo feature is combined with timbre features and improves the performance of music genre and emotion classification. Comparing with the conventional methods based on timbre features, the precision of five-genre classification is enhanced from 86.8% to 92.2% and the accuracy of four-emotion classification is increased from 86.0% to 90.5%. Based on the results of music genre/emotion classification, we design a similarity query scheme, which can speed up the similarity query process without decreasing the precision. Furthermore, all the features employed in this paper are extracted from the data of MP3 partially decoding, which significantly reduces the feature extraction time. [ABSTRACT FROM AUTHOR]