Graph-Based Image Matching for Indoor Localization

Manzo, Mario

doi:10.3390/make1030046

Cited by 10 publications

(9 citation statements)

References 38 publications

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“…This raises the need for the fusion of some high-precision and continuous positioning techniques. Inertial sensors or PDR [16] computer vision [31] could be introduced to obtain good positioning results in indoor positioning and indoor navigation.…”

Section: Discussionmentioning

confidence: 99%

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Huang

Cao

et al. 2021

IJGI

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Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

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Section: Discussionmentioning

confidence: 99%

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Huang

Cao

et al. 2021

IJGI

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“…Therefore, it is worth exploring how to effectively fuse these heterogeneous data to obtain the preference list. A number of works have been proposed to explore this trend in retrieval matching [147], [148], [149], user-item matching [150], [151], [152], entityrelation matching [153], [154], and image matching [155], [156]. However, extracting the decisive features from the problematic data such as data with missing values, noise, or outliers to complete the task of preference list inference poses a great challenge [157].…”

Section: A Preference Listmentioning

confidence: 99%

Matching Algorithms: Fundamentals, Applications and Challenges

Ren

Xia

Chen

et al. 2021

IEEE Trans. Emerg. Top. Comput. Intell.

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Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the questionanswer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching.

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“…In the image forgery, since the forged regions may be rotated, scaled, and translated in different manners, the features of the images should be invariant to these transformations. The features generated by SIFT [33] have such noteworthy characteristics and the proposed algorithm utilizes the SIFT features to represent images [34,35].…”

Section: Bag-of-features and Hamming Embedding Based Image Retrievalmentioning

confidence: 99%

Structural Correlation Based Method for Image Forgery Classification and Localization

2020

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In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.

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Graph-Based Image Matching for Indoor Localization

Cited by 10 publications

References 38 publications

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Matching Algorithms: Fundamentals, Applications and Challenges

Structural Correlation Based Method for Image Forgery Classification and Localization

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