Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Chol, Rim Kyong
- Semantic Image Description and Classification Based on Generalized Set
Abstract Views :203 |
PDF Views:7
Authors
Affiliations
1 Institute of Information Science, Kim II Sung University, KP
1 Institute of Information Science, Kim II Sung University, KP
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1781-1785Abstract
A semantic image description model based on generalized set is proposed, and the semantic similarity (distance) measure between images is presented. Semantic image information can be completely represented in this model as compared with previous researches based on vector space. The semantic image description model based on generalized set is similar to human understanding of image knowledge. For the purpose of the semantic image classification, semantic distance based on support vector machine classifier is employed. Experimental results show the validity of new method, and that the image classification accuracy is improved.Keywords
Semantic Image Description, Image Classification, Generalized Set.
References
- S. Aksoy, et al., “Learning Bayesian Classifiers for Scene Classification With a Visual Grammar”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 3, pp. 581-589, 2005.
- M.R. Boutell, J. Luo and C.M. Brown, “Scene Parsing using Region-Based Generative Models”, IEEE Transactions on Multimedia, Vol. 9, No. 1, pp. 136-146, 2007.
- H. Cheng and R. Wang, “Semantic Modeling of Natural Scenes based on Contextual Bayesian Networks”, Pattern Recognition, Vol. 43, No. 12, pp. 4042-4054, 2010.
- C. Galleguillos and S. Belongie, “Context Based Object Categorization: A Critical Survey”, Computer Vision and Image Understanding, Vol. 114, No. 6, pp. 712-722, 2010.
- Q. Li, H. Hu and Z. Shi, “Semantic Feature Extraction using Genetic Programming in Image Retrieval”, Proceedings of 17th International Conference on Pattern Recognition, pp. 302-309, 2004.
- G. Madzarov, D. Gjorgjevskij and I. Chorbev, “A Multi-Class SVM Classifier Utilizing Decision Tree”, Informatica, Vol. 33, No. 2, pp. 233-241, 2009.
- I. Nwogu, V. Govindaraju and C. Brown, “Syntactic Image Parsing using Ontology and Semantic Descriptions”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 41-48, 2010.
- G.T. Papadopoulos, V. Mezaris, I. Kompatsiaris and M.G. Strintzis, “Combining Content and Context Information for Semantic Image Analysis and Classification”, Proceedings of 15th European International Conference on Signal Processing, pp. 708-712, 2007.
- G.J. Qi, X.S. Hua, Y. Rui, J. Tang and H.J. Zhang, “Image Classification with Kernelized Spatial-Context”, IEEE Transactions on Multimedia, Vol. 12, No. 4, pp. 278-287, 2010.
- K.E. Sanders, B.P. Kettler and J.A. Hendler, “The Case for Graph-Structured Representations”, Proceedings of International Conference on Case-Based Reasoning, pp. 245-254, 1997.
- J. Vogel, “Semantic Modeling of Natural Scenes for Content-based Image Retrieval”, International Journal of Computer Vision, Vol. 72, No. 2, pp. 133-157, 2007.
- J. Yang, K. Yu, Y. Gong and T. Huang, “Linear Spatial Pyramid Matching using Sparse Coding for Image Classification”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 163-169, 2009.
- Y. Zhang, M. Yao and Z. Yuan, “Research on Methodology of Image Semantic Understanding based on Generalized Computing”, Proceedings of IEEE International Conference on Pattern Recognition and Computer Vision, pp. 113-117, 2007.
- R. Zhu, M. Yao and Y. Liu, “Image Classification Approach Based on Manifold Learning in Web Image Mining”, Proceedings of International Conference on Advanced Data Mining and Applications, pp. 780-787, 2009.