Basketball Video Research Articles

Multimodal Semantic Analysis and Annotation for Basketball Video

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 information are utilized for and scene boundary detection algorithm; this will be followed by scene classification. For audio, audio keysounds are sets of specific audio sounds related to semantic events and a classification method based on hidden Markov model (HMM) is used for audio keysound identification. Subsequently, by analyzing the multimodal information, the positions of potential semantic events, such as foul and shot at the basket, are located with additional domain knowledge. Finally, a video annotation is generated according to MPEG-7 multimedia description schemes (MDSs). Experimental results demonstrate the effectiveness of the proposed method.

Video data mining: semantic indexing and event detection from the association perspective

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

On-line knowledge- and rule-based video classification system for video indexing and dissemination

Current information and communication technologies provide the infrastructure to transport bits anywhere, but do not indicate how to easily and precisely access and/or route information at the semantic level. To facilitate intelligent access to the rich multimedia data over the Internet, we develop an on-line knowledge- and rule-based video classification system that supports automatic “indexing” and “filtering” based on the semantic concept hierarchy. This paper investigates the use of video and audio content analysis, feature extraction and clustering techniques for further video semantic concept classification. A supervised rule-based video classification system is proposed using video automatic segmentation, annotation and summarization techniques for seamless information browsing and updating. In the proposed system, a real-time scene-change detection proxy performs an initial video-structuring process by splitting a video clip into scenes. Motional, visual and audio features are extracted in real-time for every detected scene by using on-line feature-extraction proxies. Higher semantics are then derived through a joint use of low-level features along with classification rules in the knowledge base. Classification rules are derived through a supervised learning process that relies on some representative samples from each semantic category. An indexing and filtering process can now be built using the semantic concept hierarchy to personalize multimedia data based on users’ interests. In real-time filtering, multiple video streams are blocked, combined, or sent to certain channels depending on whether or not the video streams are matched with the user's profile. We have extensively experimented and evaluated the classification and filtering techniques using basketball sports video data. In particular, in our experiment, the basketball video structure is examined and categorized into different classes according to distinct motional, visual and audio characteristics features by a rule-based classifier. The concept hierarchy describing the motional/visual/audio feature descriptors and their statistical relationships are reported in this paper along with detailed experimental results using on-line sports videos.

Query by video clip

Typical digital video search is based on queries involving a single shot. We generalize this problem by allowing queries that involve a video clip (say, a 10-s video segment). We propose two schemes: (i) retrieval based on key frames follows the traditional approach of identifying shots, computing key frames from a video, and then extracting image features around the key frames. For each key flame in the query, a similarity value (using color, texture, and motion) is obtained with respect to the key frames in the database video. Consecutive key frames in the database video that are highly similar to the query key frames are then used to generate the set of retrieved video clips. (ii) In retrieval using sub-sampled frames, we uniformly subsample the query Clip as well as the database video. Retrieval is based on matching color and texture features of the subsampled frames. Initial experiments on two video databases (basketball video with approximately 16,000 frames and a CNN news video with approximately 20,000 frames) show promising results. Additional experiments using segments from one basketball video as query and a different basketball video as the database show the effectiveness of feature representation and matching schemes.