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effective in terms of better precision. Proposed method makes use of keyword clusters for query expansion. Visual features are used for detecting duplicate images in proposed method. Removing duplicates leads to further improve in precision and recall in retrieval result
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs. Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs.Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
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
. Healthcare data is easily available or can be recorded at low cost. The proposed method is used to show various relevant videos for a given user's need by keyword based label matching. The proposed method performs video data collection and speech to text conversion to create the transcription snippets. Finally, keyword based
In this paper we propose a new image search system using keyword annotations and low-level visual meta-data to generate inter-image relationships. Unlike other approaches the new system does not try to learn the degree of confidence between images and associated keywords. We rather propose to model the degree of
Automatic image annotation is a promising key to semantic-based image retrieval by keywords. Most existing automatic image annotation approaches focused on exploring the relationship between images and annotation words and neglected the semantic information of the annotated keywords. In this paper we propose a semi
The LIGVID system is designed for online interactive video shots retrieval and annotation. It uses a user-controlled combination of multiple criteria: keywords, phonetic string, similarity to example images, semantic categories, and relevance feedback strategies: visual and temporal similarity to already identified
network for labeling images with emotional keywords based on visual features only and examine an influence of used emotion filter on process of similar images retrieval. The performed experiments have shown that use of the emotion filter increases performance of the system for around 10 percent. points.
region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: (1) Local LSI resolves the heavy computation cost of LSI; (2) Pseudo Relevance Feedback doesn't need the user's
database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle
In our earlier works on VAST (visuAl & semantic image search) system, the semantic network effectively associated keywords and visual feature clusters. However, we only concerned about the construction of the semantic network before, and did not consider the updating of the semantic network. In this paper, an
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