Tagged Web Image Retrieval Re-ranking with Wikipedia-based Semantic Relatedness
نویسندگان
چکیده
منابع مشابه
Web Image Retrieval Re-ranking with Wikipedia Semantics
Nowadays, to take advantage of tags is a general tendency when users need to store or retrieve images on the Web. In this article, we introduce some approaches to calculate semantic importance of tags attached to Web images, and to make re-ranking the retrieved images according to them. We have compared the results from image re-ranking with two semantic providers, WordNet and Wikipedia. With t...
متن کاملWeb Image Retrieval Re-Ranking with Relevance Model
Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. Since content-based image retrieval is still considered very difficult, most current large-scale web image search engines exploit text and link structure to “understand” the content of the web images. However, local text information, such as caption, filenames ...
متن کاملWikipedia-based Compact Hierarchical Semantics with Application to Semantic Relatedness
A proper semantic representation of words and texts underlies many text processing tasks. In this paper, we present a novel representation of semantics which is based on an hierarchical ontology of natural concepts derived from Wikipedia articles and category system. Our method, called Compact Hierarchical Explicit Semantic Analysis (CHESA) generates compact hierarchical representations of unre...
متن کاملComputing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis
Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weight...
متن کاملA scalable re-ranking method for content-based image retrieval
Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the conte...
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ژورنال
عنوان ژورنال: Journal of Korea Multimedia Society
سال: 2011
ISSN: 1229-7771
DOI: 10.9717/kmms.2011.14.11.1491