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    188宝金博页面版: 【精品】【PAMI,2005】Performance evaluation of local invariants

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    内容提示： MIKOLAJCZYK AND SCHMID: A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS1A performance evaluation of local descriptorsKrystian Mikolajczyk and Cordelia SchmidDept. of Engineering ScienceINRIA Rh? one-AlpesUniversity of Oxford655, av. de l’EuropeOxford, OX1 3PJ38330 MontbonnotUnited KingdomFrancekm@robots. ox. ac. ukschmid@inrialpes. frAbstractIn this paper we compare the performance of descriptors computed for local interest regions, as forexample extracted by the Harris-Affine detector [32]. Many different...

    文档格式：PDF | 页数：34 | 浏览次数：17 | 上传日期：2016-12-31 17:45:47 | 文档星级：
    MIKOLAJCZYK AND SCHMID: A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS1A performance evaluation of local descriptorsKrystian Mikolajczyk and Cordelia SchmidDept. of Engineering ScienceINRIA Rhˆ one-AlpesUniversity of Oxford655, av. de l’EuropeOxford, OX1 3PJ38330 MontbonnotUnited KingdomFrancekm@robots. ox. ac. ukschmid@inrialpes. frAbstractIn this paper we compare the performance of descriptors computed for local interest regions, as forexample extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed inthe literature. However, it is unclear which descriptors are more appropriate and how their performancedepends on the interest region detector. The descriptors should be distinctive and at the same time robustto changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterionrecall with respect to precision and is carried out for different image transformations. We compareshape context [3], steerable filters [12], PCA-SIFT [19], differential invariants [20], spin images [21],SIFT [26], complex filters [37], moment invariants [43], and cross-correlation for different types ofinterest regions. We also propose an extension of the SIFT descriptor, and show that it outperforms theoriginal method. Furthermore, we observe that the ranking of the descriptors is mostly independent ofthe interest region detector and that the SIFT based descriptors perform best. Moments and steerablefilters show the best performance among the low dimensional descriptors.Index TermsLocal descriptors, interest points, interest regions, invariance, matching, recognition.I. INTRODUCTIONLocal photometric descriptors computed for interest regions have proved to be very successfulin applications such as wide baseline matching [37, 42], object recognition [10, 25], textureCorresponding author is K. Mikolajczyk,km@robots.ox.ac.uk.February 23, 2005DRAFT

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