IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

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Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9918.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9548-9551

Keyword(s):

Image Segmentation ◽

Medical Image Segmentation ◽

Experimental Result ◽

Segmentation Method ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Robust Image ◽

Multiple Clusters ◽

Single Pixel ◽

Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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Measuring Overlap-Rate in Hierarchical Cluster Merging for Image Segmentation and Ship Detection

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) ◽

10.1109/fskd.2007.392 ◽

2007 ◽

Cited By ~ 5

Author(s):

Zhiling Hong ◽

Qingshan Jiang ◽

Heshan Guan ◽

Fangfei Weng

Keyword(s):

Image Segmentation ◽

Hierarchical Cluster ◽

Ship Detection ◽

Cluster Merging ◽

Overlap Rate

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A Note on Advantages of the Fuzzy Gabor Filter in Object and Text Detection

Symmetry ◽

10.3390/sym13040678 ◽

2021 ◽

Vol 13 (4) ◽

pp. 678

Author(s):

Vladimir Tadic ◽

Tatjana Loncar-Turukalo ◽

Akos Odry ◽

Zeljen Trpovski ◽

Attila Toth ◽

...

Keyword(s):

Object Detection ◽

Detection Method ◽

Gabor Filter ◽

Low Cost ◽

Fuzzy Optimization ◽

Detection Performance ◽

Text Detection ◽

License Plate ◽

2D Gabor Filter ◽

Fine Tune

This note presents a fuzzy optimization of Gabor filter-based object and text detection. The derivation of a 2D Gabor filter and the guidelines for the fuzzification of the filter parameters are described. The fuzzy Gabor filter proved to be a robust text an object detection method in low-quality input images as extensively evaluated in the problem of license plate localization. The extended set of examples confirmed that the fuzzy optimized Gabor filter with adequately fuzzified parameters detected the desired license plate texture components and highly improved the object detection when compared to the classic Gabor filter. The robustness of the proposed approach was further demonstrated on other images of various origin containing text and different textures, captured using low-cost or modest quality acquisition procedures. The possibility to fine tune the fuzzification procedure to better suit certain applications offers the potential to further boost detection performance.

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Detection of drought events in Greece using daily precipitation

Hydrology Research ◽

10.2166/nh.2010.001 ◽

2010 ◽

Vol 41 (2) ◽

pp. 126-133 ◽

Cited By ~ 14

Author(s):

N. Kalamaras ◽

H. Michalopoulou ◽

H. R. Byun

Keyword(s):

Cluster Analysis ◽

Hierarchical Cluster Analysis ◽

Daily Precipitation ◽

Hierarchical Cluster ◽

Seasonal Distribution ◽

Drought Severity ◽

Severe Drought ◽

Analysis Method ◽

Cluster Analysis Method ◽

Insight Into

In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is calculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region. In addition, the seasonal distribution of days with moderate and severe drought is examined. Finally, the Hierarchical Cluster Analysis method is used to identify sites with similar drought features.

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A surrogate-based generic classifier for Chinese TV series reviews

Information Discovery and Delivery ◽

10.1108/idd-11-2016-0044 ◽

2017 ◽

Vol 45 (2) ◽

pp. 66-74

Author(s):

Yufeng Ma ◽

Long Xia ◽

Wenqi Shen ◽

Mi Zhou ◽

Weiguo Fan

Keyword(s):

Design Methodology ◽

Experimental Result ◽

Digital Information ◽

Data Set ◽

Content Type ◽

Category Labels ◽

Information Supply Chain ◽

Feature Selection Techniques ◽

Practical Implications

Purpose The purpose of this paper is automatic classification of TV series reviews based on generic categories. Design/methodology/approach What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated. Findings With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names. Research limitations/implications The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work. Practical implications Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing. Originality/value One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

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ALGORITHMS FOR FINGERPRINT CLASSIFICATION

NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN ◽

10.32014/2021.2224-5294.6 ◽

2021 ◽

Vol 335 (1) ◽

pp. 39-44

Author(s):

D. Lebedev ◽

A. Abzhalilova

Keyword(s):

Neural Network ◽

Gabor Filter ◽

Recognition System ◽

Personal Identification ◽

External Object ◽

Biometric Identification ◽

Fingerprint Classification ◽

Combined Application ◽

Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

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Deep Learning Based Image Segmentation with AlexNet Feature Extraction for Classification of Mammogram Images

International Journal of Pharmaceutical Research ◽

10.31838/ijpr/2021.13.01.690 ◽

2021 ◽

Vol 13 (01) ◽

Keyword(s):

Feature Extraction ◽

Image Segmentation ◽

Deep Learning ◽

Mammogram Images

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Study on Consistency Analysis in Text Categorization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.181 ◽

2014 ◽

Vol 539 ◽

pp. 181-184

Author(s):

Wan Li Zuo ◽

Zhi Yan Wang ◽

Ning Ma ◽

Hong Liang

Keyword(s):

Text Categorization ◽

Training Data ◽

Experimental Result ◽

Final Decision ◽

Consistency Analysis ◽

Training Set ◽

Weak Classifier ◽

Data Set ◽

Basic Premise

Accurate classification of text is a basic premise of extracting various types of information on the Web efficiently and utilizing the network resources properly. In this paper, a brand new text classification method was proposed. Consistency analysis method is a type of iterative algorithm, which mainly trains different classifiers (weak classifier) by aiming at the same training set, and then these classifiers will be gathered for testing the consistency degrees of various classification methods for the same text, thus to manifest the knowledge of each type of classifier. It main determines the weight of each sample according to the fact is the classification of each sample is accurate in each training set, as well as the accuracy of the last overall classification, and then sends the new data set whose weight has been modified to the subordinate classifier for training. In the end, the classifier gained in the training will be integrated as the final decision classifier. The classifier with consistency analysis can eliminate some unnecessary training data characteristics and place the key words on key training data. According to the experimental result, the average accuracy of this method is 91.0%, while the average recall rate is 88.1%.

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Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

Applied Physics Research ◽

10.5539/apr.v8n3p32 ◽

2016 ◽

Vol 8 (3) ◽

pp. 32 ◽

Cited By ~ 4

Author(s):

Olivier K. Bagui ◽

Kenneth A. Kaduki ◽

Edouard Berrocal ◽

Jeremie T. Zoueu

Keyword(s):

Cluster Analysis ◽

Principal Component ◽

Hierarchical Cluster ◽

Planar Imaging ◽

Reliable Measurement ◽

Laser Illumination ◽

Coffee Beans ◽

Chemometric Techniques ◽

Ground Coffee

Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µe and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µe. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.

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