S
Saurav Ghosh
Researcher at University of Calcutta
Publications - 37
Citations - 339
Saurav Ghosh is an academic researcher from University of Calcutta. The author has contributed to research in topics: Feature extraction & Contextual image classification. The author has an hindex of 10, co-authored 34 publications receiving 284 citations. Previous affiliations of Saurav Ghosh include Information Technology University.
Papers
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Journal ArticleDOI
Feature Extraction with Ordered Mean Values for Content Based Image Classification
TL;DR: Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification.
Book ChapterDOI
Novel Technique in Block Truncation Coding Based Feature Extraction for Content Based Image Identification
TL;DR: A new technique named Sorted Block Truncation Coding (SBTC) has been introduced in this work and has stimulated superior performance in image recognition when compared to classification and retrieval results with other existing techniques of feature extraction.
Journal ArticleDOI
Content Based Image Classification with Thepade's Static and Dynamic Ternary Block Truncation Coding
TL;DR: The authors have proposed two novel techniques of feature extraction that have exhibited higher performance efficiency compared to the state-of-the art techniques and have principally contributed to boost up classification performance.
Journal ArticleDOI
Multi technique amalgamation for enhanced information identification with content based image data
TL;DR: Three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively are introduced and shown an average increase in Precision over state-of-the art techniques.
Journal ArticleDOI
Decision fusion-based approach for content-based image classification
TL;DR: The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances and encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.