- Title
- Structural image retrieval using automatic image annotation and region based inverted file
- Creator
- Zhang, Dengsheng; Islam, Md; Lu, Guojun
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/70797
- Identifier
- vital:6501
- Identifier
-
https://doi.org/10.1016/j.jvcir.2013.07.004
- Identifier
- ISSN:1047-3203
- Abstract
- Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.
- Relation
- Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2013), p. 1087-1098
- Rights
- Copyright Academic Press
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1203 Design Practice and Management; 1905 Visual Arts and Crafts; Machine learning; Image indexing and searching; Image annotation; Vector quantization; Inverted file; Multi-instance learning; Bag-of-features; Region annotation
- Reviewed
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