This paper presents a new multiple-modality method for extracting semantic information from basketball video. The visual, motion, and audio information are extracted from video to first generate some low-level video segmentation and classification. Domain knowledge is further exploited for detecting interesting events in the basketball video. For video, both visual and motion prediction informa...

Retrieval of videos from large database using video queries plays a significant role for a lot of applications. Few researches works have been designed for retrieving relevant videos from large database using classification techniques. But, the classification performance of was not effectual for achieving higher precision and recall for video retrieval. In order to solve this limitation, Gradie...

In this paper, we propose a method for combining audio and video for segmentation and classification. The objective of segmentation is to detect the category change point such news to advertisement. The classification system classify the audio-video data into one of the predefined categories such as news, advertisement, sports, serial and movies. Mel frequency cepstral coefficients( MFCC) are u...

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervis...

Video understanding has attracted much research attention especially since the recent availability of large-scale video benchmarks. In this paper, we address the problem of multilabel video classification. We first observe that there exists a significant knowledge gap between how machines and humans learn. That is, while current machine learning approaches including deep neural networks largely...

Recently, video classification attracts intensive research efforts. However, most existing works are based on framelevel visual features, which might fail to model the temporal information, e.g. characteristics accumulated along time. In order to capture video temporal information, we propose to analyse features in frequency domain transformed by discrete Fourier transform (DFT features). Frame...