Image Retrieval System

An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System

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Online image retrieval system is more important in the real world environment where the users submitted their queries to retrieve the images. The main aim of mining is extracting valuable information from huge volume of textual or image datasets. The user queries are mined to enhance the web searching which is extremely useful to improve the retrieval effectiveness. The image retrieval accuracy is measured based on the user satisfaction level. Automatic annotation is utilized for operating over high dimensional text documents or image contents. Different online image retrieval techniques are available like Latent Semantic Indexing (LSI), Markovian Semantic Indexing (MSI) etc., but these techniques cannot retrieve the images accurately due to the lack of semantic knowledge about the user submitted high level keywords. Moreover the LSI and MSI have less image retrieval accuracy. Hence, this research work focussed on the improvement of the accuracy of the image retrieval system.


Sangeetha Seenivasan


Dr. Sangeetha Seenivasan is Head and Assistant Professor, Department of CA and IT. She has completed her B.Sc (CS)., M.Sc (CT)., M.Phil. and Ph.D. in Bharathiar University. She got Gold Medal on her PG Degree. She guided 17 M.Phil Scholars. She published more than 20 papers in national and international journals.

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LAP LAMBERT Academic Publishing


Image annotation, Optimization, Feature extraction, hashing, Product Quantization

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