Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm

Rong, Hanxiao; Ramírez-Serrano, Alejandro; Guan, Lei; Gao, Yanbin

doi:10.1109/access.2020.3025193

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…In which kernel functions are transformed into feature space, to estimate the correct number of clusters. The combination of depth and semantic information of images [20] improve the accurate identification of initial center value.…”

Section: Clustering Methodsmentioning

confidence: 99%

A Study on Image Segmentation Method for Image Processing

Prabu

Gnanasekar²

2021

Recent Trends in Intensive Computing

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Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.

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Section: Clustering Methodsmentioning

confidence: 99%

A Study on Image Segmentation Method for Image Processing

Prabu

Gnanasekar²

2021

Recent Trends in Intensive Computing

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“…where E z ( ) refers to the Gibbs energy of the motion image, U k ( ) refers to the probability of smooth separation, and r a ( ) refers to the edge weight function of the foreground and background of the motion image (Cao et al, 2020;Rong et al, 2020). The operation time of the improved image is determined according to the energy of the motion mask in the specified motion frame.…”

Section: Motion Image Segmentationmentioning

confidence: 99%

Target Detection for Motion Images Using the Improved YOLO Algorithm

Zhang

2023

Journal of Database Management

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The images of motion states are time-varying, and when actually detecting their internal motion targets, the formed detection frames overlap, resulting in small confidence values for the detection frames and low accuracy of the detection results. To address this problem, the authors propose a target detection for motion image using the improved YOLO algorithm. First, the YOLO algorithm is improved using deformable convolution; the edge weights of the front and back views within the image are collated, and the motion image is segmented using the improved YOLO algorithm. Second, the structure formed by the initial convolution is used as the initial detection frame structure, the parallel cross-ratio value is set, the overlap generated by the detection frame is controlled, the parameters of the detection frame compression processing are output, the threshold trigger value relationship is constructed, and finally, the detection of the motion image target is realized. The results show that the target false detection rate of the proposed method is only about 15%. The detection a priori frame height value is 80 pixels, and the average detection time consumed is 6.8ms, which proves that the proposed algorithm can be widely used in motion image target detection to improve the detection level.

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“…As a classical clustering algorithm arising in 1960s (Gaddam et al, 2007; Hu et al, 2021; Jia et al, 2016; Liang et al, 2016; Mat Isa et al, 2009; Rong et al, 2020), K‐means has been widely used on large datasets, though it is less efficient on non‐spherical data. While those non‐spherical algorithms, such as DBSCAN and spectral clustering algorithm, have some common shortcomings: high calculation cost, difficult initial parameters selection (He et al, 2014; Kumari et al, 2017; Mahesh Kumar & Rama Mohan Reddy, 2016; Tsai & Huang, 2012) and high temporal complexity.…”

Section: Introductionmentioning

confidence: 99%

Improved K‐means algorithm for clustering non‐spherical data

He¹,

Wang³

et al. 2022

Expert Systems

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As one of the commonly used data mining algorithms, K-means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non-spherical data. An improved K-means algorithm (IK-means) is proposed to enhance clustering efficiency for non-spherical data. The original dataset is clustered into a relatively larger number of high-density sub-clusters, and the final result is obtained by merging connected sub-clusters respectively. The connectivity among sub-clusters is evaluated by the sub-clusters density and the nearest distance class between sub-clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK-means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK-means algorithm is faster than DBSCAN and KGFCM.

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Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm

Cited by 21 publications

References 30 publications

A Study on Image Segmentation Method for Image Processing

A Study on Image Segmentation Method for Image Processing

Target Detection for Motion Images Using the Improved YOLO Algorithm

Improved K‐means algorithm for clustering non‐spherical data

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