The Seeding Algorithm for Spherical k-Means Clustering with Penalties

Ji, Sai; Xu, Dianguo; Guo, Longkun; Li, Min; Zhang, Dongmei

doi:10.1007/978-3-030-27195-4_14

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…AP is the average accuracy, calculated by plotting the PR curve and then selecting the average of the maximum accuracy greater than or equal to each R value. mAP is the average of the AP for each type of scrap, as in Equation (8).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

“…Shabbir, S. et al used a CNN (convolutional neural network) [ 7 ] and SEEDS (Super-pixels Extracted via Energy-Driven Sampling) [ 8 ] algorithm to achieve the automatic classification of different types of nonferrous metal waste, which provides new possibilities for the automatic classification of metal wastes with high accuracy. Gao, Z.J.…”

Section: Introductionmentioning

confidence: 99%

“…An improved lightweight architecture for real-time small target detection is proposed, based on the You Only Look Once (YOLO) model by combining channel-wise attention block, space-attention block, and multi-branch convolutional neural network structure [6]. Shabbir, S. et al used a CNN (convolutional neural network) [7] and SEEDS (Superpixels Extracted via Energy-Driven Sampling) [8] algorithm to achieve the automatic classification of different types of nonferrous metal waste, which provides new possibilities for the automatic classification of metal wastes with high accuracy. Gao, Z.J.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Gao,

Liu,

Chui

et al. 2024

Sensors

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In this work, an object detection method using variable convolution-improved YOLOv8 is proposed to solve the problem of low accuracy and low efficiency in detecting spanning and irregularly shaped samples. Aiming at the problems of the irregular shape of a target, the low resolution of labeling frames, dense distribution, and the ease of overlap, a deformable convolution module is added to the original backbone network. This allows the model to deal flexibly with the problem of the insufficient perceptual field of the target corresponding to the detection point, and the situations of leakage and misdetection can be effectively improved. In order to solve the issue that small target detection is susceptible to image background and noise interference, the Sim-AM (simple parameter-free attention mechanism) module is added to the backbone network of YOLOv8, which enhances the attention to the underlying features and, thus, improves the detection accuracy of the model. More importantly, the Sim-AM module does not need to add parameters to the original network, which reduces the computation of the model. To address the problem of complex model structures that can lead to slower detection, the spatial pyramid pooling of the backbone network is replaced with focal modulation networks, which greatly simplifies the computation process. The experimental validation was carried out on the scrap steel dataset containing a large number of targets of multiple shapes and sizes. The results showed that the improved YOLOv8 network model improves the AP (average precision) by 2.1%, the mAP (mean average precision value) by 0.8%, and reduces the FPS (frames per second) by 5.4, which meets the performance requirements of real-time industrial inspection.

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Gao,

Liu,

Chui

et al. 2024

Sensors

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“…As a well-known clustering problem, given n data samples/points, the k-means clustering [1][2][3][4] aims to partition the data points into k clusters, such that the overall squared Euclidean distance between each data point and its closest cluster centroid (mean of the cluster) is minimized. Due to the Non-deterministic Polynomial (NP)-hardness of the k-means problem [5][6][7] , several approximation algorithms were proposed [8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithm for the $k$-Means Problem with Must-Link and Cannot-Link Constraints

Jia,

Guo,

Liao

et al. 2023

Tsinghua Sci. Technol.

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Constrained clustering, such as k-means with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information as the constraints, has been extensively studied recently, due to its broad applications in data science and AI. Despite some heuristic approaches, there has not been any algorithm providing a non-trivial approximation ratio to the constrained k-means problem. To address this issue, we propose an algorithm with a provable approximation ratio of O.log k/ when only ML constraints are considered. We also empirically evaluate the performance of our algorithm on real-world datasets having artificial ML and disjoint CL constraints. The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy.

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The Seeding Algorithm for Spherical k-Means Clustering with Penalties

Cited by 2 publications

References 13 publications

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Efficient Algorithm for the $k$-Means Problem with Must-Link and Cannot-Link Constraints

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