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A short review on different clustering techniques and their applications
In modern world, we have to deal with huge volumes of data which include image, video,
text and web documents, DNA, microarray gene data, etc. Organizing such data into rational …
text and web documents, DNA, microarray gene data, etc. Organizing such data into rational …
Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is
often introduced to an objective function to improve the robustness of the FCM algorithm for …
often introduced to an objective function to improve the robustness of the FCM algorithm for …
Superpixel-based fast fuzzy C-means clustering for color image segmentation
A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely
used for grayscale and color image segmentation. However, most of them are time …
used for grayscale and color image segmentation. However, most of them are time …
[PDF][PDF] Deepcut: Joint subset partition and labeling for multi person pose estimation
This paper considers the task of articulated human pose estimation of multiple people in real
world images. We propose an approach that jointly solves the tasks of detection and pose …
world images. We propose an approach that jointly solves the tasks of detection and pose …
Motion segmentation & multiple object tracking by correlation co-clustering
Models for computer vision are commonly defined either wrt low-level concepts such as
pixels that are to be grouped, or wrt high-level concepts such as semantic objects that are to …
pixels that are to be grouped, or wrt high-level concepts such as semantic objects that are to …
Coordinate Descent Method for -means
-means method using Lloyd heuristic is a traditional clustering method which has played a
key role in multiple downstream tasks of machine learning because of its simplicity …
key role in multiple downstream tasks of machine learning because of its simplicity …
Machine learning-based self-powered acoustic sensor for speaker recognition
Herein, we report a new platform of machine learning-based speaker recognition via the
flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant …
flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant …
Anchor-based fast spectral ensemble clustering
Ensemble clustering can obtain better and more robust results by fusing multiple base
clusterings, which has received extensive attention. Although many representative …
clusterings, which has received extensive attention. Although many representative …
A survey on the utilization of Superpixel image for clustering based image segmentation
Superpixel become increasingly popular in image segmentation field as it greatly helps
image segmentation techniques to segment the region of interest accurately in noisy …
image segmentation techniques to segment the region of interest accurately in noisy …
Adaptive morphological reconstruction for seeded image segmentation
Morphological reconstruction (MR) is often employed by seeded image segmentation
algorithms such as watershed transform and power watershed, as it is able to filter out seeds …
algorithms such as watershed transform and power watershed, as it is able to filter out seeds …