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Báo cáo hóa học: " Research Article Combining Global and Local Information for Knowledge-Assisted Image Analysis and Classification"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Combining Global and Local Information for Knowledge-Assisted Image Analysis and Classification | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 45842 15 pages doi 10.1155 2007 45842 Research Article Combining Global and Local Information for Knowledge-Assisted Image Analysis and Classification G. Th. Papadopoulos 1 2 V. Mezaris 2 I. Kompatsiaris 2 and M. G. Strintzis1 2 1 Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki 54006 Greece 2 Centre for Research and Technology Hellas CERTH Informatics and Telematics Institute Thermi 57001 Greece Received 8 September 2006 Revised 23 February 2007 Accepted 2 April 2007 Recommended by Ebroul Izquierdo A learning approach to knowledge-assisted image analysis and classification is proposed that combines global and local information with explicitly defined knowledge in the form of an ontology. The ontology specifies the domain of interest its subdomains the concepts related to each subdomain as well as contextual information. Support vector machines SVMs are employed in order to provide image classification to the ontology subdomains based on global image descriptions. In parallel a segmentation algorithm is applied to segment the image into regions and SVMs are again employed this time for performing an initial mapping between region low-level visual features and the concepts in the ontology. Then a decision function that receives as input the computed region-concept associations together with contextual information in the form of concept frequency of appearance realizes image classification based on local information. A fusion mechanism subsequently combines the intermediate classification results provided by the local- and global-level information processing to decide on the final image classification. Once the image subdomain is selected final region-concept association is performed using again SVMs and a genetic algorithm GA for optimizing the mapping between the image regions and the selected subdomain concepts .