Fast K-Means technique for hyper-spectral image segmentation by multiband reduction

Kumar, V. Saravana; Sivaprakasam, S. Anantha; Rengasari, Naganathan; Kavitha, S.

doi:10.1556/606.2019.14.3.19

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Padmavathi and angadurai used the color feature of the leaf image to segment the leaf image into multiple parts by color [9]. Kumar et al proposed a new remote sensing hyperspectral scene segmentation technique [10]. But their proposed AI segmentation algorithm is not accurate enough.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Zhang

Lian

Liu

2022

Contrast Media & Molecular Imaging

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The knee joint is the second largest joint in the human body, with a wide range of functional activities and strong support for the human body. Moreover, the cartilage of the knee joint is hyaline cartilage, which is relatively brittle, so it is most vulnerable to trauma. In clinical work, the damage of articular cartilage is a disease with a high rate of orthopedic visits. In this paper, all the experimental group cases included in the observation were patients with acute articular cartilage injury or OA diagnosed by knee arthroscopy. All experimental groups and control groups did not have any strenuous exercise one day before MRI (magnetic resonance imaging), and they sat for 30 minutes before the examination. Conventional scanning sagittal FSE-T1WI, FSE-T2WI, FS-FSE-T1WI, FS-FSE-T2WI, FS-PDWI, and coronal FS-PDWI sequence. In the normal control group, after the T2 color map was generated in the workstation, the articular cartilage was divided on the midsagittal plane, and the patellar cartilage and tibial plateau were roughly divided into upper, middle, lower and anterior, middle, and posterior thirds. In order to ensure the maximum comparability of the results, an artificial intelligence segmentation algorithm is used to divide the region of interest equally, and the central part of each partition is selected as much as possible for measurement. The T2 values of the three partitions of each cartilage were measured one by one and averaged. For the comparison results of T2 value of cartilage in the same part: according to patellar cartilage, femoral cartilage, and tibial cartilage, the P values are 0.973, 0.150, and 0.525, respectively. Therefore, early detection and early treatment of articular cartilage injury are of great significance to the performance of athletes’ competition level and the extension of sports life.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Zhang

Lian

Liu

2022

Contrast Media & Molecular Imaging

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“…1 p refers to new point to be classified either dark square label or empty circle label. Here, p belongs to the dark square class if k 5 1; if k 5 5, then it is classified as the small circle class due to majority vote rule [6,7].…”

Section: Related Workmentioning

confidence: 99%

Predicting students’ academic performance using a modified kNN algorithm

Jawthari

Stoffová

2021

Pollack

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The target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-nearest-neighbors algorithm that calculates the distances values of categorical (nominal) variables without encoding them. A student’s academic performance dataset is used for testing the enhanced algorithm. It shows that the proposed algorithm outperforms standard one that needs nominal variables encoding to calculate the distance between the nominal variables. The results show the proposed algorithm preforms 14% better than standard one in accuracy, and it is not sensitive to outliers.

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“…Many clustering algorithms have been proposed so far: k-means, single link, CURE (meaning Clustering Using Representatives), DBSCAN (meaning Density-Based Spatial Clustering of Applications with Noise), and Expectation Maximization are well-known examples; see [15]. Several clustering algorithms have been successfully applied in contexts like information retrieval [16], bioinformatics [17], medicine [18], image segmentation [19], and cybersecurity [20], among others. An important class of clustering algorithms is graphbased clustering algorithms.…”

Section: Graph-based Clusteringmentioning

confidence: 99%

Effective and Generalizable Graph-Based Clustering for Faces in the Wild

Chang

Pérez-Suárez

González-Mendoza

2019

Computational Intelligence and Neuroscience

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Face clustering is the task of grouping unlabeled face images according to individual identities. Several applications require this type of clustering, for instance, social media, law enforcement, and surveillance applications. In this paper, we propose an effective graph-based method for clustering faces in the wild. The proposed algorithm does not require prior knowledge of the data. This fact increases the pertinence of the proposed method near to market solutions. The experiments conducted on four well-known datasets showed that our proposal achieves state-of-the-art results, regarding the clustering performance, also showing stability for different values of the input parameter. Moreover, in these experiments, it is shown that our proposal discovers a number of identities closer to the real number existing in the data.

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Fast K-Means technique for hyper-spectral image segmentation by multiband reduction

Cited by 3 publications

References 12 publications

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Predicting students’ academic performance using a modified kNN algorithm

Effective and Generalizable Graph-Based Clustering for Faces in the Wild

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