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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
In classical image classification approaches, low-level features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selection and distance measurement during the clustering process. In this paper, we propose an approach to generate visual keyword and combine both visual
The main obstacle in realizing semantic-based image retrieval is from the web that semantic description of an image is difficult to capture in low-level features. Text based keywords can be generated from web documents to capture semantic information for narrowing down the search space. We use an effective approach to
. First, the related textual information associated with Web images is identified as the candidate annotations for Web images. Second, the word co-occurrence is utilized to eliminate irrelevant keywords for improving the annotation accuracy. Then, the keyword-based association analysis is exploited to further discover
Finder system. We show that improved text extraction results in the retrieval of a larger number of relevant images for a set of domain-relevant keyword searches.
Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current
designed and implemented to resolve the problem of crossing language queries and retrieving images processes. It can greatly reduce lot of time and effort for the search. The experiments on diverse queries on Yahoo images search have shown that the proposed scheme can improve the images results for non-English keyword
scenes by checking the discovered cross-media correlation. To make these two modalities comparable, photos related to the visited scenic spots are retrieved from image search engines, by the keywords extracted from text-based schedules. Sequences of key frames and retrieved photos are represented as visual word histograms
Hausdorff distance (HD) and its modifications provides one of the best approaches for matching of binary images. This paper proposes a formalism generalizing almost all of these HD based methods. Numerical experiments for searching words in binary text images are carried out with old Bulgarian typewritten text, printed Bulgarian Chrestomathy from 1884 and Slavonic manuscript from 1574.
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
largely rely on keywords instead of geometry figure images. This study focuses on plane geometry figure (PGF) image retrieval with the aim of retrieving relevant geometry images that contain more structural information than a question text stem. To fully use geometrical properties, a Bag-of-shapes (BoS) method is proposed to
Existence of countless digital images has given rise to image retrieval in many applications. Conventional image databases being text-annotated pose two major problems of keywords for images and complexity. Hence, retrieval systems based on image's visual content are more desirable [1]. The content based image
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