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- Title
A Novel Indexing and Image Annotation Structure for Efficient Image Retrieval.
- Authors
Prasanthi, B.; Pabboju, Suresh; Vasumathi, D.
- Abstract
The present-day commercial web-based search engines have adopted web-based image search to improve accuracy in image data retrieval. Though re-ranking is conventionally considered as an effective process for determining the status of web-based picture search engines, it suffers from a few deficiencies. Hence, some classification techniques, especially novel image re-ranking system, have been proposed to implement query image re-ranking with semantic signatures in web-based image data retrieval, which automatically retrieves results based on visual semantic features for different query or keyword expansions. In essence, search-based image index annotation is a process that effectively achieves more number of matched similar images with weak label data presentation to found noise and unsatisfied picture indexing and formation. In order to overcome this limitation in index-based image retrieval, it seeks to define effective label indexing for formulating web images, using machine learning approaches. To access efficient image with annotation, we introduce and implement an effective and novel computational evaluation approach, i.e., non-training-based label index refinement approach with convex optimization classification for applying large data preprocessing for weakly labeled data in image indexing. We also develop an approximation-based grouping algorithm to improve precision and recall efficiency in large web-based image retrieval tasks. Our experimental results show efficient image indexing with different experimental studies on large scale web-based images. Our experiments in search-based annotation yield scalability measure results in the range of 85-90% as compared to the existing approaches in image retrieval.
- Subjects
IMAGE retrieval; MACHINE learning
- Publication
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ), 2018, Vol 43, Issue 8, p4203
- ISSN
2193-567X
- Publication type
Article
- DOI
10.1007/s13369-017-2827-1