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In this paper, we proposed a new 3D object retrieval method based on the visual keywords. In our method, the visual keywords are generated from the clusters of relative angle context distribution, which provides a statistical shape context that captures local shape characters and is also rotational and scale invariant
To alleviate the known semantic gap, it is necessary to integrate the two-modal parts of Web images, i.e. the low-level visual features and high-level semantic concepts (which are usually represented by keywords), for Web image retrieval. In this paper, we associate the keyword and visual features of Web images from a
in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
Content-based image retrieval (CBIR) has been adopted as a complementary technique to the keyword-based image search. Relevance feedback (RFB) is considered as an effective means to bridge the gap between the designated features and the run-time semantics on a CBIR system. Like many other interactive system, a good
Recently, the development of 3D model database systems and retrieval components are becoming increasingly important due to a rapidly growing amount of available 3D models. This has made the retrieval for specific 3D models become a vital issue. Unfortunately, traditional keyword searching techniques are not always
appear on websites with other text contents which can deliver important information about the image semantics. Popular image search engines use text contents surrounding the image to generate annotation keywords. Also emphasized text contents like headlines are assumed to be important description providers. Otherwise we
This paper presents a case study of an image retrieval system based on a notion of similarity between images in a multimedia database and where a user request can be an image file or a keyword. The CBIR (content based image retrieval) system, the current system of search for information (SSI) -e.g. PEIR, MIRC, MIR
Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavily on human
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images
The amount of multimedia information is rapidly increasing due to digital cameras. To interpret semantic of image, many researcher use keywords as textual annotation. Image semantic information retrieval became attractive for many peoples. Concept recognition is a key problem in semantic information searching. A new
(dasiacolorpsila,dasiatexturepsila and dasiashapepsila) and high-level semantic meanings (e.g., dasiaskypsila,dasiabeachpsila). Namely, AIA techniques annotates images with many noisy key words. Refinement process has been appeared in these days and it tries to remove noisy keywords by using Knowledge-base and boosting candidate
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