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approach has a limit as only the annotations of found images during the interaction are updated. In this paper we introduce a novel method of semi-automatic annotation. The method is using visual feature representations of keywords which are improved during the region-based relevance feedback. The experiments show that this
This paper presents an integrated approach to automatically provide an overview of content on Thai websites based on tag cloud. This approach is intended to address the information overload issue by presenting the overview to users in order that they could assess whether the information meets their needs. The approach has incorporated Web content extraction, Thai word segmentation, and information...
Database (HMDB), a collection of realistic video clips. The detection and localization paradigm we introduce uses a keyword model for detecting key activities or gestures in a video sequence. This process is analogous to the use of keyword or key-phrase detection in speech processing. The method learns models for the
Manual tagging has an important impact to performance of image/video searching by keyword. However, users usually mark tags only landmarks are as on only a few images in library and leave most contents untagged. If landmarks from different places are look alike, it is hard to distinguish even though surroundings are
this module and early results of CBIR enabled the combination of content-based retrieval and keyword retrieval. It made some improvements to the retrieval performance and narrowed the gap of semantics. Experimental results demonstrated that this project can to a certain extent help users more precisely retrieve to their
topic, object and attribute dictionaries. Eight kinds of text are extracted as image semantic source from Web pages. Combining with semantic dictionaries, image semantic keywords can be extracted from the eight kinds of text. The strategy of extracting image semantics is better than existing technique, which is better than
Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework. In region-based image annotation, keywords are usually
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