{"title":"基于Mahalanobis距离和加权KNN图的图像匹配算法","authors":"Du Bo, Zhangguan-liang, Cuixiao-long","doi":"10.1109/ICISCE.2015.34","DOIUrl":null,"url":null,"abstract":"A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation using Mahalanobis distance is proposed. The similarity evaluated by Mahalanobis distance is embedded into WGTM algorithm under the constraint of median distance and angular distance. Then point pairs were obtained through iteratively eliminating the outliers. Experimental results on synthetic data and real-world data demonstrate that the proposed algorithm is effective and robust.","PeriodicalId":356250,"journal":{"name":"2015 2nd International Conference on Information Science and Control Engineering","volume":"567 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"An Algorithm of Image Matching Based on Mahalanobis Distance and Weighted KNN Graph\",\"authors\":\"Du Bo, Zhangguan-liang, Cuixiao-long\",\"doi\":\"10.1109/ICISCE.2015.34\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation using Mahalanobis distance is proposed. The similarity evaluated by Mahalanobis distance is embedded into WGTM algorithm under the constraint of median distance and angular distance. Then point pairs were obtained through iteratively eliminating the outliers. Experimental results on synthetic data and real-world data demonstrate that the proposed algorithm is effective and robust.\",\"PeriodicalId\":356250,\"journal\":{\"name\":\"2015 2nd International Conference on Information Science and Control Engineering\",\"volume\":\"567 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 2nd International Conference on Information Science and Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2015.34\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 2nd International Conference on Information Science and Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2015.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Algorithm of Image Matching Based on Mahalanobis Distance and Weighted KNN Graph
A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation using Mahalanobis distance is proposed. The similarity evaluated by Mahalanobis distance is embedded into WGTM algorithm under the constraint of median distance and angular distance. Then point pairs were obtained through iteratively eliminating the outliers. Experimental results on synthetic data and real-world data demonstrate that the proposed algorithm is effective and robust.
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