A modified Efficient Manifold Ranking Algorithm for Large Database Image Retrieval

2020 ◽  
Vol 23 (1) ◽  
pp. 79-89
Author(s):  
Quy Hoang Van ◽  
Huy Tran Van ◽  
Huy Ngo Hoang ◽  
Tuyet Dao Van ◽  
Sergey Ablameyko
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Ranking Algorithm ◽  
Large Database ◽  
Manifold Ranking ◽  
Fcm Clustering ◽  
Hard Clustering ◽  
Anchor Points ◽  
Large Image Databases

The efficient manifold ranking (EMR) algorithm is used quite effectively in content-based image retrieval (CBIR) for large image databases where images are represented by multiple low-level features to describe about the color, texture and shape. The EMR ranking algorithm requires steps to determine anchor points of the image database by using the k-means hard clustering and the accuracy of the ranking depends strongly on the selected anchor points. This paper describes a new result based on a modified Fuzzy C-Means (FCM) clustering algorithm to select anchor points in the large database in order to increase the efficiency of manifold ranking specially for the large database cases. Experiments have demonstrated the effectiveness of the proposed algorithm for the issue of building an anchor graph, the set of anchor points determined by this novel lvdc-FCM algorithm has actually increased the effective of manifold ranking and the quality of images query results which retrieved of the CBIR.

scholarly journals Exploring Rotation and Scale Invariant Features in Image Pla- giarism Detection Using Manifold-Ranking Algorithm

2018 ◽  
Vol 7 (4) ◽  
pp. 2663
Author(s):  
Simindokht Jahangard
Keyword(s):  
High Performance ◽  
Nearest Neighbor ◽  
Feature Point ◽  
Ranking Algorithm ◽  
Scale Invariant ◽  
Manifold Ranking ◽  
Image Transforms ◽  
Scientific Papers ◽  
Supervised Methods

Plagiarism, as a crucial offense especially in academia, not only is well-known problem in text but also is becoming widespread in image. In this work, the performance of manifold-ranking, known as robust method among semi-supervised methods, has been investigated by using twelve different features. As its high performance is attributed to the quality of constructed graph, we applied robust k-regular nearest neighbor (k-RNN) graph in the framework of manifold-ranking based retrieval. Among all tested feature point detectors and descriptors, Root-SIFT, the feature point ones, due to it is invariant to an array of image transforms, is the most reliable feature for calculating image similarity. The database consisting of images from scientific papers containing four popular benchmark test images served to test these methods. 

An Efficient Web Image Annotation Ranking Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 81-87
Author(s):  
Zheng Liu
Keyword(s):  
Image Retrieval ◽  
Real World ◽  
Retrieval System ◽  
Image Annotation ◽  
Region Growing ◽  
Ranking Algorithm ◽  
Manifold Ranking ◽  
Image Retrieval System ◽  
Data Points ◽  
Web Images

Existing image annotation approaches mainly concentrate on achieving annotation results. Annotation order has not been taken into account carefully. As orderly annotation list could enhance the performance of image retrieval system, it is of great importance to rank annotations. This paper presents an algorithm to rank Web image annotating results. For an annotated Web image, we firstly partition the image by a region growing method. Secondly, relevance degree between two annotations is estimated through considering both semantic similarity and image content. Next, the regions of unlabeled image to be ranked serve as queries and annotations are used as the data points to be ranked. And then, manifold-ranking algorithm is executed to get the ordered annotation list. Experiments conducted on real-world Web images through NDCG metric demonstrate the effectiveness of the proposed approach.

FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM

1994 ◽  
Vol 02 (03) ◽  
pp. 343-350 ◽  
Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Two Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Hard Clustering

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

scholarly journals A Fast Algorithm to Initialize Cluster Centroids in Fuzzy Clustering Applications

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 446
Author(s):  
Zeynel Cebeci ◽  
Cagatay Cebeci
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Computing Time ◽  
Fcm Clustering ◽  
Data Points ◽  
Time Required ◽  
Initialization Algorithm ◽  
Predetermined Number

The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In this paper, we propose the InoFrep, a novel data-dependent initialization algorithm for improving computational efficiency and robustness in prototype-based hard and fuzzy clustering. The InoFrep is a single-pass algorithm using the frequency polygon data of the feature with the highest peaks count in a dataset. By using the Fuzzy C-means (FCM) clustering algorithm, we empirically compare the performance of the InoFrep on one synthetic and six real datasets to those of two common initialization methods: Random sampling of data points and K-means++. Our results show that the InoFrep algorithm significantly reduces the number of iterations and the computing time required by the FCM algorithm. Additionally, it can be applied to multidimensional large datasets because of its shorter initialization time and independence from dimensionality due to working with only one feature with the highest number of peaks.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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