Abstract：Most of the traditional image texture recognition method focuses on spectrum. In this paper, the layering idea is used based on granular computing theory to recognized the image texture features. Firstly, the rough granular theory is presented and the rough granular space model is constructed by introducing the concept of granular edges and layered entropy. Secondly, a kind of similarity calculation method is established based on granular edges and layered entropy. Then, a method of image texture recognition is put forward. The proposed method improves the practicality of the model and simplifies the calculation of texture recognition by synchronous identification and retrieval. Finally, the simulation result show that the performance of image retrieval is improved. The validity of the proposed method is testified by the contrast with other methods.
[1] Miao Duoqian,Wang Guoyin,Liu Qing.Granular Computing: Past,Present and Prospects.Beijing,China: Science Press,2007(in Chinese) (苗夺谦,王国胤,刘 清.粒计算:过去、现在与展望.北京:科学出版社,2007) [2] Stepaniuk J,Skowron A.Ontological Framework for Approximation // Proc of the 10th International Conference on Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing.Regina,Canada,2005: 718-727 [3] Slowinski R,Vanderpooten D.A Generalized Definition of Rough Approximations Based on Similarity.IEEE Trans on Knowledge and Data Engineering,2000,12(2): 331-336 [4] Doherty P,Grabowski M,Lukaszewicz W,et al.Towards a Framework for Approximate Ontologies.Fundamental Information,2003,57(2): 147-165 [5] Antón-Rodríguez M,Díaz-Pernas F J,Díez-Higuera J F,et al.Recognition of Coloured and Textured Images through a Multi-Scale Neural Architecture with Orientational Filtering and Chromatic Diffusion.Neurocomputing,2009,72(16/17/18): 3713-3725 [6] Ilea D E,Whelan P F.Image Segmentation Based on the Integration of Color-Texture Descriptors-A Review.Pattern Recognition,2011,44(10/11): 2479-2501 [7] Zheng Zheng.Image Texture Recognition Based on Tolerance Granular Space.Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition,2009,21(4): 484-489 (in Chinese) (郑 征.基于相容粒度空间模型的图像纹理识别.重庆邮电大学学报:自然科学版,2009,21(4): 484-489) [8] Wang Xiangyang,Chen Jingwei,Yu Yongjian.An Edge-Based Color Image Retrieval by Using Multiple Features.Pattern Recognition and Artificial Intelligence,2010,23(2): 216-221 (in Chinese) (王向阳,陈景伟,于永健.一种基于彩色边缘综合特征的图像检索算法.模式识别与人工智能,2010,23(2): 216-221) [9] Deselaers T,Keysers D,Ney H.Features for Image Retrieval: An Experimental Comparison.Information Retrieval,2008,11(2): 77-107 [10] Chen Bo,Dai Qiuping.Image Segmentation Based on Geometric Active Contour Model.Pattern Recognition and Artificial Intelligence,2010,23(2): 186-190 (in Chinese) (陈 波,代秋平.基于几何活动轮廓模型的图像分割.模式识别与人工智能,2010,23(2): 186-190) [11] Kolaczyk E D,Ju J,Gopal S.Multiscale,Multigranular Statistical Image Segmentation.Journal of the American Statistical Association,2005,100(472): 1358-1369 [12] Gandhi V,Kang J M,Shekhar S.Context Inclusive Function Evaluation: A Case Study with EM-Based Multi-Scale Multi-Granular Image Classification.Knowledge and Information Systems,2009,21(2): 231-247 [13] Xu Jiucheng,Li Xiaoyan,Li Shuangqun,et al.Feature Images Retrieval Method of Tolerance Granular-Based Multi-Level Texture.Journal of Guangxi Normal University: Natural Science Edition,2011,29(1): 186-190 (in Chinese) (徐久成,李晓艳,李双群,等.基于相容粒的多层次纹理特征图像检索方法.广西师范大学学报:自然科学版,2011,29(1): 186-190) [14] Yao Xiaokun,Qiu Taorong,Bai Xiaoming.Granular Computing Method Based on Access to Approximate Ontology.Journal of Nanchang University: Natural Science,2009,33(6): 595-598 (in Chinese) (姚晓昆,邱桃荣,白小明.近似本体获取的粒计算方法.南昌大学学报: 理科版,2009,33(6): 595-598) [15] Lei Baoquan,Yang Lihua,Cheng Yongmei,et al.Natural Object Recognition Algorithm Based on SVM and Color/ Texture Combination Features.Computer Science,2009,36(10): 274-277 (in Chinese) (雷宝权,杨丽华,程咏梅,等.基于 SVM与颜色/纹理组合特征的景物识别算法.计算机科学,2009,36(10): 274-277)
2012, Vol. 25 
