{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T01:45:51Z","timestamp":1648518351573},"reference-count":9,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2006,7]]},"abstract":"<jats:p> In the field of content-based image retrieval, there exist a gap between low-level descriptions of image content and the semantic needs of users to query image databases. This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g. sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g. color, texture, shape, etc.). Particularly, we consider outdoor images and automatically classify their regions based on their semantics using a support vector machines (SVMs). The SVMs learns the semantics of specified classes from specific low-level feature of the test image regions. Image regions are, first, segmented using a hill-climbing approach. Then, those regions are classified by the SVMs. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach. <\/jats:p>","DOI":"10.1142\/s021946780600229x","type":"journal-article","created":{"date-parts":[[2006,8,1]],"date-time":"2006-08-01T13:00:26Z","timestamp":1154437226000},"page":"357-375","source":"Crossref","is-referenced-by-count":0,"title":["REGION-BASED SEMANTIC IMAGE CLASSIFICATION"],"prefix":"10.1142","volume":"06","author":[{"given":"ZAHER","family":"AL AGHBARI","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Sharjah, P.O. Box 27272, Sharjah, UAE"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","volume-title":"Query by Image and Video Content: The QBIC System","author":"Flickner M.","year":"1995"},{"key":"rf5","volume":"16","author":"Natsev A.","journal-title":"IEEE Transaction on Knowledge and Data Engineering"},{"key":"rf6","volume":"16","author":"Krishnapuram R.","journal-title":"IEEE Transaction on Knowledge and Data Engineering"},{"key":"rf7","volume":"13","author":"Jeng F.","journal-title":"IEEE Transaction on Image Processing"},{"key":"rf16","first-page":"679","volume":"8","author":"Canny J.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"rf17","first-page":"1406","volume":"86","author":"Kubo M.","journal-title":"IEICE Transactions on Information and Systems"},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1006\/acha.2000.0343"},{"key":"rf19","volume-title":"Statistical Learning Theory","author":"Vapnik V.","year":"1998"},{"key":"rf20","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"}],"container-title":["International Journal of Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021946780600229X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T10:26:34Z","timestamp":1565173594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S021946780600229X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,7]]},"references-count":9,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,20]]},"published-print":{"date-parts":[[2006,7]]}},"alternative-id":["10.1142\/S021946780600229X"],"URL":"https:\/\/doi.org\/10.1142\/s021946780600229x","relation":{},"ISSN":["0219-4678","1793-6756"],"issn-type":[{"value":"0219-4678","type":"print"},{"value":"1793-6756","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,7]]}}}