{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:00:08Z","timestamp":1761897608092,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T00:00:00Z","timestamp":1631318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2017CFB591"],"award-info":[{"award-number":["2017CFB591"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research Foundation of Education Department of Guizhou Province","award":["[2021]305"],"award-info":[{"award-number":["[2021]305"]}]},{"name":"Doctoral Scientific Research Foundation of Guizhou Normal University","award":["2014"],"award-info":[{"award-number":["2014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the task of interactive image segmentation, the Inside-Outside Guidance (IOG) algorithm has demonstrated superior segmentation performance leveraging Inside-Outside Guidance information. Nevertheless, we observe that the inconsistent input between training and testing when selecting the inside point will result in significant performance degradation. In this paper, a deep reinforcement learning framework, named Inside Point Localization Network (IPL-Net), is proposed to infer the suitable position for the inside point to help the IOG algorithm. Concretely, when a user first clicks two outside points at the symmetrical corner locations of the target object, our proposed system automatically generates the sequence of movement to localize the inside point. We then perform the IOG interactive segmentation method for precisely segmenting the target object of interest. The inside point localization problem is difficult to define as a supervised learning framework because it is expensive to collect image and their corresponding inside points. Therefore, we formulate this problem as Markov Decision Process (MDP) and then optimize it with Dueling Double Deep Q-Network (D3QN). We train our network on the PASCAL dataset and demonstrate that the network achieves excellent performance.<\/jats:p>","DOI":"10.3390\/s21186100","type":"journal-article","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T21:48:01Z","timestamp":1631483281000},"page":"6100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Inside Point Localization with Deep Reinforcement Learning for Interactive Object Segmentation"],"prefix":"10.3390","volume":"21","author":[{"given":"Guoqing","family":"Li","sequence":"first","affiliation":[{"name":"College of Physical Science and Technology, Central China Normal University, NO. 152 Luoyu Road, Wuhan 430079, China"},{"name":"Key Laboratory of Quark and Lepton Physics (MOE) and College of Physics Science and Technology, Central China Normal University, NO. 152 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoping","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Physical Science and Technology, Central China Normal University, NO. 152 Luoyu Road, Wuhan 430079, China"},{"name":"Key Laboratory of Quark and Lepton Physics (MOE) and College of Physics Science and Technology, Central China Normal University, NO. 152 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chanchan","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science, Guizhou Normal University, The University Town, Guian New Area, Guiyang 550025, China"},{"name":"Center for RFID and WSN Engineering, Department of Education, Guizhou Normal University, The University Town, Guian New Area, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","article-title":"Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning","volume":"37","author":"Wang","year":"2018","journal-title":"IEEE Trans. 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