{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T04:05:41Z","timestamp":1748923541414,"version":"3.41.0"},"reference-count":18,"publisher":"Fuji Technology Press Ltd.","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Adv. Comput. Intell. Intell. Inform.","JACIII"],"published-print":{"date-parts":[[2014,7,20]]},"abstract":"<jats:p>In this paper, we perform image labeling based on the probabilistic integration of local and global features. Several conventional methods label pixels or regions using features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on local viewpoints. To overcome this problem, we propose an image labeling method that utilizes both local and global features. We compute the posterior probability distributions of the local and global features independently, and they are integrated by the product. To compute the probability of the global region (entire image), Bag-of-Words is used. In contrast, local cooccurrence between color and texture features is used to compute local probability. In the experiments, we use the MSRC21 dataset. The result demonstrates that the use of global viewpoint significantly improves labeling accuracy.<\/jats:p>","DOI":"10.20965\/jaciii.2014.p0511","type":"journal-article","created":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T06:11:59Z","timestamp":1460614319000},"page":"511-517","source":"Crossref","is-referenced-by-count":0,"title":["Image Labeling by Integration of Local Co-Occurrence Histogram and Global Features"],"prefix":"10.20965","volume":"18","author":[{"given":"Takuto","family":"Omiya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Department of Electronical and Electronic Engineering, Meijo University, 1-501 Shiogamaguchi, Tenpaku-ku, Nagoya, Aichi 468-8502, Japan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhiro","family":"Hotta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2014,7,20]]},"reference":[{"key":"key-10.20965\/jaciii.2014.p0511-1","doi-asserted-by":"crossref","unstructured":"K. Barnard and D. Forsyth, \u201cLearning the semantics of words and pictures,\u201d Proc. Int. Conf. on Computer Vision, Vol.2, pp. 408-415, 2001.","DOI":"10.1109\/ICCV.2001.937654"},{"key":"key-10.20965\/jaciii.2014.p0511-2","unstructured":"J. Lafferty, A. McCallum, and F. Pereira, \u201cConditional random fields: probabilistic models for segmenting and labeling sequence data,\u201d Proc. Int. Conf. on Machine Learning, pp. 282-289, 2001."},{"key":"key-10.20965\/jaciii.2014.p0511-3","doi-asserted-by":"crossref","unstructured":"J. Shotton, J. Winn, C. Rother, and A. Criminisi, \u201cTextonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation,\u201d Proc. European Conf. on Computer Vision, pp. 1-15, 2006.","DOI":"10.1007\/11744023_1"},{"key":"key-10.20965\/jaciii.2014.p0511-4","doi-asserted-by":"crossref","unstructured":"S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, \u201cMulticlass segmentation with relative location prior,\u201d Int. J. of Computer Vision, Vol.80, pp. 300-316, 2008.","DOI":"10.1007\/s11263-008-0140-x"},{"key":"key-10.20965\/jaciii.2014.p0511-5","doi-asserted-by":"crossref","unstructured":"Z. Tu, \u201cAuto-context and its application to high-level vision tasks,\u201d Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2008.","DOI":"10.1109\/CVPR.2008.4587436"},{"key":"key-10.20965\/jaciii.2014.p0511-6","doi-asserted-by":"crossref","unstructured":"T. Omiya and K. Hotta, \u201cImage labeling using integration of local and global features,\u201d Proc. Int. Conf. on Pattern Recognition Applications and Methods, Barcelona, Spain, pp. 613-618, 2013.","DOI":"10.5220\/0004334606130618"},{"key":"key-10.20965\/jaciii.2014.p0511-7","doi-asserted-by":"crossref","unstructured":"V. Vapnik, \u201cThe name of statistical learning theory,\u201d Springerverlag New York, 1995.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"key-10.20965\/jaciii.2014.p0511-8","unstructured":"G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, \u201cVisual categorization with bags of keypoints,\u201d Proc. ECCV Workshop on Statistical Learning in Computer Vision, 2004."},{"key":"key-10.20965\/jaciii.2014.p0511-9","doi-asserted-by":"crossref","unstructured":"E. Nowak, F. Jurie, and B. Triggs, \u201cSampling strategies for bag-offeatures image classification,\u201d Proc. European Conf. on Computer Vision, pp. 490-503, 2006.","DOI":"10.1007\/11744085_38"},{"key":"key-10.20965\/jaciii.2014.p0511-10","doi-asserted-by":"crossref","unstructured":"C. Galleguillos, A. Rabinovich, and S. Belongie, \u201cObject categorization using co-occurrence,\u201d Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2008.","DOI":"10.1109\/CVPR.2008.4587799"},{"key":"key-10.20965\/jaciii.2014.p0511-11","doi-asserted-by":"crossref","unstructured":"L. Ladicky, C. Russell, P. Kohli, and P. Torr, \u201cGraph cut based inference with co-occurrence statistics,\u201d Proc. European Conf. on Computer Vision, pp. 239-253, 2010.","DOI":"10.1007\/978-3-642-15555-0_18"},{"key":"key-10.20965\/jaciii.2014.p0511-12","doi-asserted-by":"crossref","unstructured":"T. Ojala, \u201cMultiresolution gray-scale and rotation invariant texture classification with local binary patterns,\u201d Pattern Analysis and Machine Intelligence, Vol.24, pp. 971-987, 2002.","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"key-10.20965\/jaciii.2014.p0511-13","doi-asserted-by":"crossref","unstructured":"R. Arandjelovic and A. Zisserman, \u201cThree things everyone should know to improve object retrieval,\u201d Proc. Computer Vision and Pattern Recognition, pp. 2911-2918, 2012.","DOI":"10.1109\/CVPR.2012.6248018"},{"key":"key-10.20965\/jaciii.2014.p0511-14","doi-asserted-by":"crossref","unstructured":"D. Lowe, \u201cObject recognition from local scale-invariant features,\u201d Proc. Int. Conf. on Computer Vision, Vol.2, pp. 1150-1157, 1999.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"key-10.20965\/jaciii.2014.p0511-15","doi-asserted-by":"crossref","unstructured":"L. Fei-Fei and P. Perona, \u201cA Bayesian hierarchical model for learning natural scene categories,\u201d Proc. Computer Vision and Pattern Recognition, Vol.2, pp. 524-531, 2005.","DOI":"10.1109\/CVPR.2005.16"},{"key":"key-10.20965\/jaciii.2014.p0511-16","doi-asserted-by":"crossref","unstructured":"J. Zhang, M. Marzaklek, S. Lazebnik, and C. Schmid, \u201cLocal features and kernels for classification of texture and object categories: a comprehensive study,\u201d Int. J. of Computer Vision, Vol.73, pp. 213-238, 2007.","DOI":"10.1007\/s11263-006-9794-4"},{"key":"key-10.20965\/jaciii.2014.p0511-17","doi-asserted-by":"crossref","unstructured":"O. Chapelle, P. Haffner, and V. Vapnik, \u201cSupport vector machines for histogram-based image classification,\u201d Neural Networks, Vol.10, pp. 1055-1064, 1999.","DOI":"10.1109\/72.788646"},{"key":"key-10.20965\/jaciii.2014.p0511-18","unstructured":"LIBSVM, http:\/\/www.csie.ntu.edu.tw\/\u02dccjlin\/libsvm\/ [Accessed April 12, 2012]."}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/phyosetsu.php?ppno=JACII001800040006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T16:46:33Z","timestamp":1748882793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii001800040511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,7,20]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2014,7,20]]},"published-print":{"date-parts":[[2014,7,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2014.p0511","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"type":"electronic","value":"1883-8014"},{"type":"print","value":"1343-0130"}],"subject":[],"published":{"date-parts":[[2014,7,20]]}}}