A Privacy-Preserving and Efficient k-Nearest Neighbor Query and Classification Scheme Based on k-Dimensional Tree for Outsourced Data

Du, Juan; Fuling, Bian

doi:10.1109/access.2020.2986245

Cited by 18 publications

(13 citation statements)

References 33 publications

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“…At first, a K-D (K-Dimensional) tree of point features is constructed [27], and all data are divided into left and a right subtrees according to their spatial location. Then, the same operations are conducted on the data in the subtrees until all points have been processed.…”

Section: Point Feature Matchingmentioning

confidence: 99%

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Meng

Hou

et al. 2022

Sensors

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Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

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Section: Point Feature Matchingmentioning

confidence: 99%

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Meng

Hou

et al. 2022

Sensors

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“…A preprocessing step that converts RGB images to grayscale ones is required before feature extraction. For classification, we use the SVM classifier [32] and the k-NN classifier Cunningham and Delany [30] combined with the KD-Tree [31]. In fact, the KD-Tree is a structure used to organize data in a K-dimensional space according to their spatial distribution.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

“…For feature extraction, we utilize two different features: (1) hand-crafted features by exploiting the most used and efficient descriptors, which are the SURF Özdemir et al [27] and the HOG [28], and (2) learned features extracted from the final layers of the pre-trained Inception-v3 architecture model [29]. As regards classification, our suggested method is evaluated by using the k Nearest Neighbors (k-NN) Cunningham and Delany [30] combined with the K Dimensional Tree (KD-Tree) [31] and Support Vector Machine (SVM) [32] classifiers as classical methods and the pre-trained Inception-v3 model as a deep learning method.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Ouannes¹,

Khalifa²,

Amara³

2021

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In the recent years, the face recognition task has attracted the attention of researchers due to its efficiency in several domains such as surveillance and access control. Unfortunately, there are multiple challenges that decrease the performance of face recognition. Partial occlusion is the most challenging one since it often causes a great lack of information. The main purpose of this paper is to prove that facial reconstruction improves the results of facial recognition compared to de-occlusion and full-face recognition in the presence of occlusion. Our objective is to achieve occluded-face recognition, de-occluded-face recognition, and reconstructed-face recognition. Regarding face reconstruction, we introduce two different methods based on Laplacian pyramid blending and CycleGANs. In order to validate our work, we perform two different feature extraction techniques: hand-crafted features and learned features exploiting the final layers of a pre-trained deep architecture model. The experimental results on the EURECOM Kinect Face Dataset (EKFD) and the IST-EURECOM Light Field Face Database (IST-EURECOM LFFD) show that the proposed face reconstruction approach, compared with the face de-occlusion and occluded-face recognition ones, clearly improves the face recognition task. Our method boosts the classification performance in comparison with the state-of-the-art methods, achieving 94.66% on EKFD and 72.35% on IST-EURECOM LFFD.

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“…The Best Bin First (BBF) search algorithm is a search algorithm developed for K-D tree structures. It outperforms the K-D tree search algorithm in terms of processing high-dimensional features [20]. It pushes points that can be traced into a sequence, and sorts these points, according to their distances from a hyperplane.…”

Section: Rough Matching Strategy 231 Bbf Search Strategymentioning

confidence: 99%

Satellite-Borne Optical Remote Sensing Image Registration Based on Point Features

Hou

Gao

Wang

et al. 2021

Sensors

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Since technologies in image fusion, image splicing, and target recognition have developed rapidly, as the basis of many image applications, the performance of image registration directly affects subsequent work. In this work, for rich features of satellite-borne optical imagery such as panchromatic and multispectral images, the Harris corner algorithm is combined with the scale invariant feature transform (SIFT) operator for feature point extraction. Our rough matching strategy uses the K-D (K-Dimensional) tree combined with the BBF (Best Bin First) method, and the similarity measure is the nearest neighbor/the second-nearest neighbor ratio. Finally, a triangle-area representation (TAR) algorithm is utilized to eliminate false matches in order to ensure registration accuracy. The performance of the proposed algorithm is compared with existing popular algorithms. The experimental results indicate that for visible light and multi-spectral satellite remote sensing images of different sizes and different sources, the proposed algorithm in this work is excellent in accuracy and efficiency.

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A Privacy-Preserving and Efficient k-Nearest Neighbor Query and Classification Scheme Based on k-Dimensional Tree for Outsourced Data

Cited by 18 publications

References 33 publications

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Satellite-Borne Optical Remote Sensing Image Registration Based on Point Features

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