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Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of
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.
img(Anaktisi) is a C#/.NET content base image retrieval application suitable for the Web. It provides efficient retrieval services for various image databases using as a query a sample image, an image sketched by the user and keywords. The image retrieval engine is powered by innovative compact and effective
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
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