International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Aug. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 88
Paper Published : 1239
No. of Authors : 3115
  Journal Paper

Paper Title
User Query Analysis Using Annotation Mining

Abstract—To provide a higher precision of image retrieval from image search engines. We provide a novel method to construct an Aggregate Markov Chain through which the relevance between the keywords is defined and the queries are also used to automatically annotate the images. Assuming such a system, the user’s queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The user’s queries arealso used to automatically annotate the images. The queries formed by the users of a search engine are semantically refined, the keywords representing concise semantics when compared to text in documents or other vocabulary related presentations. We introduce the Monrovian Semantic Indexing (MSI), a new based image retrieval. The properties of MSI make it particularly suitable for ABIR tasks when the per image annotation data is limited. Here we use LDA (Linear Discriminant Analysis) method to filter redundant images and store it in a separate database thereby reducing the database sizefor efficient retrieval of images. We also provide image editing options like enhancing or reducing the image size also provided. The annotation mining and image ranking along with manual mapping of images are stored in the database repository. Here we use Support Vector machine algorithm for image ranking process and Annotation mining to map the image withtheir relevant names is proposed.

Author - A.P.Narmadha, S.Jeevitha, A.Kavitha, T.Kalaichel

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| Published on 2014-05-16
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