A Fast C-GIST Based Image Retrieval Method for Vision-Based Indoor Localization

Ma, Lin; Xue, Hao; Jia, Tong; Tan, Xiaoyang

doi:10.1109/vtcspring.2017.8108338

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The query image is initially compared with cluster centers of the scenes with high similarity, which boosts the retrieval efficiency. The experimental results indicate that the image retrieval by our multi-layer search tree is more efficient compared with other single-layer and multi-layer clustering algorithms, such as the mean shift-based [36], C-Gist [37], Bayesian estimation-based [62], Bayesian estimation-KLT, and mean shift-KLT algorithms. Multi-layer clustering algorithms outperform single-layer algorithms because the KLT feature tracking-based algorithm subdivides the scene-level clusters and groups images at the sub-scene level, resulting in reducing the comparison between the query and database images.…”

Section: Discussionmentioning

confidence: 92%

“…The success rate effectively reflects the impact of the scene-level clustering algorithm on the performance of image retrieval. The scene-level clustering algorithms of database images can be divided into two categories: one is based on the method of detecting change points of visual features (such as the proposed HCIR algorithm in this paper, the mean shift-based algorithm, and the Bayesian estimation-based algorithm), and another is clustering a fixed number of database images (such as the C-GIST algorithm [37]). In the C-GIST algorithm, five consecutive database images are grouped into one cluster, and the cluster center is a feature vector of the image that is located at the center position of each cluster.…”

Section: Experimental Results Of Hierarchical Image Retrieval and Vis...mentioning

confidence: 99%

“…Based on Gabor features, a global feature named Gist was proposed by Oliva et al, designed for scene recognition [35]. Gist features have already been widely used in indoor image retrieval and have achieved some remarkable results, which indicate that Gist features have the potential to address the image retrieval problem in visual localization [36,37].…”

Section: Related Workmentioning

confidence: 99%

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Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

et al. 2022

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Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency.

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Section: Discussionmentioning

confidence: 92%

Section: Experimental Results Of Hierarchical Image Retrieval and Vis...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

et al. 2022

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“…It is an important problem to construct an effective fingerprint database with limited resources on the premise of ensuring localization efficiency and accuracy. Monocular images can be used to build offline databases by shooting videos [ 13 , 14 , 15 ], constructing landmark feature descriptors [ 16 ], and obtaining fingerprint information at reference points [ 17 , 18 ]. When building an offline database, we prefer getting images with accurate positions and poses.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry

et al. 2023

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In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost.

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EfiLoc: large-scale visual indoor localization with efficient correlation between sparse features and 3D points

2021

Vis Comput

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A Fast C-GIST Based Image Retrieval Method for Vision-Based Indoor Localization

Cited by 6 publications

References 11 publications

Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry

EfiLoc: large-scale visual indoor localization with efficient correlation between sparse features and 3D points

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