{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:08:36Z","timestamp":1754154516926,"version":"3.41.2"},"reference-count":3,"publisher":"Emerald","issue":"4\/5","license":[{"start":{"date-parts":[[2010,7,8]],"date-time":"2010-07-08T00:00:00Z","timestamp":1278547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,7,8]]},"abstract":"Purpose<\/jats:title>Content\u2010based image retrieval (CBIR) technologies offer many advantages over purely text\u2010based image search. However, one of the drawbacks associated with CBIR is the increased computational cost arising from tasks such as image processing, feature extraction, image classification, and object detection and recognition. Consequently CBIR systems have suffered from a lack of scalability, which has greatly hampered their adoption for real\u2010world public and commercial image search. At the same time, paradigms for large\u2010scale heterogeneous distributed computing such as grid computing, cloud computing, and utility\u2010based computing are gaining traction as a way of providing more scalable and efficient solutions to large\u2010scale computing tasks.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>This paper presents an approach in which a large distributed processing grid has been used to apply a range of CBIR methods to a substantial number of images. By massively distributing the required computational task across thousands of grid nodes, very high through\u2010put has been achieved at relatively low overheads.<\/jats:p><\/jats:sec>Findings<\/jats:title>This has allowed one to analyse and index about 25 million high resolution images thus far, while using just two servers for storage and job submission. The CBIR system was developed by Imense Ltd and is based on automated analysis and recognition of image content using a semantic ontology. It features a range of image\u2010processing and analysis modules, including image segmentation, region classification, scene analysis, object detection, and face recognition methods.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>In the case of content\u2010based image analysis, the primary performance criterion is the overall through\u2010put achieved by the system in terms of the number of images that can be processed over a given time frame, irrespective of the time taken to process any given image. As such, grid processing has great potential for massively parallel content\u2010based image retrieval and other tasks with similar performance requirements.<\/jats:p><\/jats:sec>","DOI":"10.1108\/00012531011074681","type":"journal-article","created":{"date-parts":[[2010,8,21]],"date-time":"2010-08-21T07:18:11Z","timestamp":1282375091000},"page":"438-446","source":"Crossref","is-referenced-by-count":4,"title":["Large\u2010scale grid computing for content\u2010based image retrieval"],"prefix":"10.1108","volume":"62","author":[{"given":"Chris","family":"Town","sequence":"first","affiliation":[]},{"given":"Karl","family":"Harrison","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022021420265078700_b1","doi-asserted-by":"crossref","unstructured":"Mo\u015bcicki, J.T., Brochu, F., Ebke, J., Egede, U., Elmsheuser, J., Harrison, K., Jones, R.W.L., Lee, H.C., Liko, D., Maier, A., Muraru, A., Patrick, G.N., Pajchel, K., Reece, W., Samset, B.H., Slater, M.W., Soroko, A., Tan, C.L., van der Ster, D.C. and Williams, M. (2009), \u201cGanga: a tool for computational\u2010task management and easy access to grid resources\u201d, published in arXiv:0902.2685v1, submitted to Comp. Phys. Comm, 2009.","DOI":"10.1016\/j.cpc.2009.06.016"},{"key":"key2022021420265078700_b2","doi-asserted-by":"crossref","unstructured":"Town, C.P. (2006), \u201cOntological inference for image and video analysis\u201d, International Journal of Machine Vision and Applications, Vol. 17 No. 2, pp. 94\u2010115.","DOI":"10.1007\/s00138-006-0017-3"},{"key":"key2022021420265078700_b3","doi-asserted-by":"crossref","unstructured":"Town, C.P. and Sinclair, D.A. (2004), \u201cLanguage\u2010based querying of image collections on the basis of an extensible ontology\u201d, International Journal of Image and Vision Computing, Vol. 22 No. 3, pp. 251\u201067.","DOI":"10.1016\/j.imavis.2003.10.002"}],"container-title":["Aslib Proceedings"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/00012531011074681","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/00012531011074681\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/00012531011074681\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:37:08Z","timestamp":1753357028000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ajim\/article\/62\/4-5\/438-446\/122080"}},"subtitle":[],"editor":[{"given":"Vanda","family":"Broughton","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2010,7,8]]},"references-count":3,"journal-issue":{"issue":"4\/5","published-print":{"date-parts":[[2010,7,8]]}},"alternative-id":["10.1108\/00012531011074681"],"URL":"https:\/\/doi.org\/10.1108\/00012531011074681","relation":{},"ISSN":["0001-253X"],"issn-type":[{"type":"print","value":"0001-253X"}],"subject":[],"published":{"date-parts":[[2010,7,8]]}}}