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This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously
This paper presents an audio keywords detection method for highlight retrieval in basketball video. The keywords contain shoes squeaking sound, speech, cheer, long whistle and short whistle, which correspond to basketball game events. After feature analysis, the Simple Excellent Feature Combination based on Pearson
events in soccer video using on-screen texts. The proposed approach is completely automatic and independent to languages since it recommends the users to query events by keywords in image-form which are agents of clusters of stationary on-screen textboxes which are localized and extracted properly by a novel mechanism
mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context-related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically
To exploit co-occurrence patterns among features and target semantics while keeping the simplicity of the keyword-based visual search, a novel reranking methods is proposed. The approach, ordinal reranking, reranks an initial search list by utilizing the co-occurrence patterns via the ranking functions such as ListNet
scenes by checking the discovered cross-media correlation. To make these two modalities comparable, photos related to the visited scenic spots are retrieved from image search engines, by the keywords extracted from text-based schedules. Sequences of key frames and retrieved photos are represented as visual word histograms
Semantic soccer video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords
, in the first phase video segmentation and key frame detection is performed to extract meaningful key frames. Secondly, OCR, HOG and ASR algorithms are applied over the keyframe to extract textual keyword. In the third phase, Color, Texture and Edge features are also extracted. Finally, search similarity measure is
Despite the tremendous importance and availability of large video collections, support for video retrieval is still rather limited and is mostly tailored to very concrete use cases and collections. In image retrieval, for instance, standard keyword search on the basis of manual annotations and content-based image
In this paper, we propose a multimodal query suggestion method for video search engine which can leverage multimodal processing to improve the quality of search results. When users type general or ambiguous textual queries, our system provides keyword suggestions and representative image examples in an easy-to-use
videos and generate corresponding MPEG-7 description files. Subsequently, it establishes distributed index of the MPEG-7 files and distributed storage of video files separately. The system provides numerous web query interfaces, including keywords semantic expansion query, semantic graph query and natural language query
degree of relevancy for the user than is currently available with conventional methods, for example, using matching keywords. We describe here our method and the relation between the scenes and discuss a prototype system.
submission date, number of views, ranking position, description keywords, political inclination of the submitter, the political message in the video, and comments associated with the video, we construct a picture of how online video medium was used during the last congressional political campaign. Our analysis takes into
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