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Use of semantic content is one of the important tasks in image analysis, which needs to be addressed for improving image retrieval effectiveness. We present a method to assign multiple keywords to image using SVMs. Images are divided into three-level regions called global image, semi-global images and sub-images. For
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
where our approach is tested on images retrieved from Google keyword based image search engine. The results show that a combination of our approach as a local image descriptor with another global descriptor outperforms other approaches.
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
retrieved from the CBIR system. Metadata of the result images which are similar to the query image are extracted from the metadata database. From the resulting metadata, common keywords are extracted and proposed as the keywords for the query image.
This paper proposes an ingenious and fast method to classify videos into fixed broad classes, which would assist searching and indexing using semantic keywords. The model extracts constituent frames from videos and maps low-level features extracted these frames to high-level semantics. We use color, structure and
The intention of image retrieval systems is to provide retrieved results as close to users' expectations as possible. However, users' requirements vary from each other in various application scenarios for the same concept and keywords. In this paper, we introduce a personalized image retrieval model driven by users
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