Modified Model in Content-based Flower Image Retrieval

摘要：

Flower image retrieval is a significant and challenging problem in content-based image retrieval. We had systematic and overall researches on flower images, including repetitive images filtering, regional segmentation, feature extraction and image retrieval based on SVM, etc. Firstly, in order to ensure retrieval results, we propose a repetitive images detection algorithm based on Canny edge to filter repetitive flower images. Aiming at image segmentation, we proposed an adaptive segmentation algorithm based on 2RGB mixed color model to segment flower images. On the basis of multi-feature fusion strategy, we propose a weighted invariant moment feature based on HSV color model to extract shape feature from flower images, and then we also propose an edge LBP operator which combine texture and shape information. Final experimental results on flower dataset reveal that our algorithms are effective.

关键词： Flower image retrieval CBIR Regional segmentation Feature extraction Multi-features fusion

作者: Xiao Ke Shaozi Li Xiaofen Chen

作者单位: Cognitive Science Department, Fujian Key Laboratory of Brain-like Intelligent Systems Xiamen Univers Computer Science Department Xiamen University Xiamen, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 183-188

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）