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Open AccessArticle

Graph-Based Image Matching for Indoor Localization

Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, Italy
Mach. Learn. Knowl. Extr. 2019, 1(3), 785-804; https://doi.org/10.3390/make1030046
Received: 6 June 2019 / Revised: 8 July 2019 / Accepted: 13 July 2019 / Published: 15 July 2019
(This article belongs to the Section Learning)
Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to propose an alternative graph-based image representation. An image is encoded by a Region Adjacency Graph (RAG), based on Multicolored Neighborhood (MCN) clustering. This representation is integrated into a Content-Based Image Retrieval (CBIR) system, designed for the vision-based positioning task. The image matching phase, in the CBIR system, is managed with an approach of attributed graph matching, named the extended-VF algorithm. Evaluated in a context of indoor localization, the proposed system reports remarkable performance. View Full-Text
Keywords: content-based image retrieval; clustering; attributed graph matching; image-based localization content-based image retrieval; clustering; attributed graph matching; image-based localization
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Manzo, M. Graph-Based Image Matching for Indoor Localization. Mach. Learn. Knowl. Extr. 2019, 1, 785-804.

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