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TPAMI.2019.2913640.pdf | 12 MB | Adobe PDF | 見る/開く |
タイトル: | Properties of Mean Shift |
著者: | Yamasaki, Ryoya Tanaka, Toshiyuki |
著者名の別形: | 山﨑, 遼也 田中, 利幸 |
キーワード: | Mode estimation mode clustering mean shift algorithm conditional mean shift algorithm subspace constrained mean shift algorithm |
発行日: | 1-Sep-2020 |
出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
誌名: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
巻: | 42 |
号: | 9 |
開始ページ: | 2273 |
終了ページ: | 2286 |
抄録: | We study properties of the mean shift (MS)-type algorithms for estimating modes of probability density functions (PDFs), via regarding these algorithms as gradient ascent on estimated PDFs with adaptive step sizes. We rigorously prove convergence of mode estimate sequences generated by the MS-type algorithms, under the assumption that an analytic kernel function is used. Moreover, our analysis on the MS function finds several new properties of mode estimate sequences and corresponding density estimate sequences, including the result that in the MS-type algorithm using a Gaussian kernel the density estimate monotonically increases between two consecutive mode estimates. This implies that, in the one-dimensional case, the mode estimate sequence monotonically converges to the stationary point nearest to an initial point without jumping over any stationary point. |
著作権等: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/254200 |
DOI(出版社版): | 10.1109/TPAMI.2019.2913640 |
PubMed ID: | 31034409 |
出現コレクション: | 学術雑誌掲載論文等 |
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