scholarly journals Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jing Luo ◽  
Dan Song ◽  
Chunbo Xiu ◽  
Shuze Geng ◽  
Tingting Dong
Keyword(s):  
Classification Accuracy ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Curvelet Transform ◽  
Texture Features ◽  
Classification Problem ◽  
Fingerprint Matching ◽  
Gray Level ◽  
Fingerprint Classification

Fingerprint classification is an important indexing scheme to reduce fingerprint matching time for a large database for efficient large-scale identification. The abilities of Curvelet transform capturing directional edges of fingerprint images make the fingerprint suitable to be classified for higher classification accuracy. This paper presents an efficient algorithm for fingerprint classification combining Curvelet transform (CT) and gray-level cooccurrence matrix (GLCM). Firstly, we use fast discrete Curvelet transform warping (FDCT_WARPING) to decompose the original image into five scales Curvelet coefficients and construct the Curvelet filter by Curvelet coefficients relationship at adjacent scales to remove the noise from signals. Secondly, we compute the GLCMs of Curvelet coefficients at the coarsest scale and calculate 16 texture features based on 4 GLCMs. Thirdly, we construct 49 direction features of Curvelet coefficients at the other four scales. Finally, fingerprint classification is accomplished byK-nearest neighbor classifiers. Extensive experiments were performed on 4000 images in the NIST-4 database. The proposed algorithm achieves the classification accuracy of 94.6 percent for the five-class classification problem and 96.8 percent for the four-class classification problem with 1.8 percent rejection, respectively. The experimental results verify that proposed algorithm has higher recognition rate than that of wavelet-based techniques.

scholarly journals Segmentation and Classification of Jaw Bone CT images using Curvelet based Texture features

1970 ◽  
Vol 9 (1) ◽  
pp. 33-43
Author(s):  
TK Reddy ◽  
N Kumaravel
Keyword(s):  
Success Rate ◽  
Bone Quality ◽  
Classification Accuracy ◽  
Bone Structure ◽  
Medical Science ◽  
Recognition Rate ◽  
Texture Features ◽  
Gray Level ◽  
Mineral Density ◽  
Jaw Bone

The evaluation of jaw bone trabecular structure and quality could be useful for characterization and response of the bone for dental implants. Current clinical methods for assessment of bone quality at the implant sites largely depend on assessing bone mineral density using Dual energy X-ray absorptionometry. However, this does not provide any information about bone structure which is considered to be an equally important factor in assessing bone quality. This paper presents a novel approach for computer analysis of trabecular (or cancellous) bone structure using multiresolution based texture analysis to evaluate changes taking place in the architecture of bone with age and gender. The findings are compared with Hounsfield Units measured from the CT machine at different sites, which is a standard reference. Fifty patients were subjected to clinical CT to obtain the CT number and texture based architectural parameters respectively. In each site texture features were extracted using gray level co-occurrence matrices (GLCM), Run length matrices, Histogram and curvelet based statistical & co occurrence analysis. A very difficult problem in classification techniques is the choice of features to distinguish between classes. However the performance of any classifier is not optimized when all features are used. The feature optimization problem is addressed using Principle component analysis in terms of the best recognition rate and the optimal number of features. Testing this on a series of 120 image sections of trabecular bone with normal, partial and total edentulous patients correctly classified over 90% of the porous bone group with an overall accuracy of 87.8%-95.2%.The results shows that by using the Classification & Regression Tree approach the combination of the features from gray level and Ist order statistics achieved overall classification accuracy in the range of 87.8- 90.24%. Features selected from the curvelet based co occurrence matrix performed better with overall classification accuracy of 92.89%.In order to increase the success rate the classification is done using the combination of curvelet statistical features and curvelet co occurrence features as feature vector and using this, a mean success rate of 95.2% is obtained. Keywords: Multiresolution analysis; Texture features; curvelets; Computed Tomography; Regression analysis; GLRLM. DOI: 10.3329/bjms.v9i1.5229 Bangladesh Journal of Medical Science Vol.09 No.1 Jan 2010 33-43

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 9 (2S3) ◽  
pp. 590-592
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1358-1369
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Block-based cloud classification with statistical features and distribution of local texture features

2015 ◽  
Vol 8 (3) ◽  
pp. 1173-1182 ◽  
Author(s):  
H.-Y. Cheng ◽  
C.-C. Yu
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Local Texture ◽  
Cloud Classification ◽  
Block Based ◽  
Combined Feature ◽  
Neighbor Classifier

Abstract. This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose performing block division and block-based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including the k-nearest neighbor classifier, Bayesian classifier, and support vector machine.

scholarly journals A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 2502 ◽  
Author(s):  
G. S. N. Murthy ◽  
Srininvasa Rao. V ◽  
T. Veerraju
Keyword(s):  
Nearest Neighbor ◽  
Texture Classification ◽  
Texture Features ◽  
Significant Feature ◽  
Gray Level ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Novel Approach ◽  
Occurrence Matrix

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods.

scholarly journals Block based cloud classification with statistical features and distribution of local texture features

2014 ◽  
Vol 7 (11) ◽  
pp. 11771-11798 ◽  
Author(s):  
H.-Y. Cheng ◽  
C.-C. Yu
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Local Texture ◽  
Cloud Classification ◽  
Block Based ◽  
Combined Feature ◽  
Neighbor Classifier

Abstract. This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose to perform block division and block based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including k-nearest neighbor classifier, Bayesian classifier, and support vector machine in this paper.

scholarly journals Clothing Product Reviews Mining Based on Machine Learning

2015 ◽  
Vol 11 (9) ◽  
pp. 71
Author(s):  
Qing Hong ◽  
Peifei Feng ◽  
Zhichao Cheng
Keyword(s):  
Machine Learning ◽  
Maximum Entropy ◽  
Classification Accuracy ◽  
Large Scale ◽  
Classification Problem ◽  
Product Reviews ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
In The Beginning ◽  
Multi Classification

This paper used the method of machine learning to study clothing product reviews classification based on big enterprise data. Taking Taobao clothing reviews as the object, it firstly excavated review themes from reviews corpus by association rules, and then searched review themes related to the categories by a method of mutual information to enrich the review themes. In the process of building classification models, commonly used SVM classifiers were studied in the beginning. After training and verification of a large amount of data, the classification accuracy reached 90.597%. In order to further improve the classification accuracy, the maximum entropy model was built by adopting the maximum entropy algorithm, on the basis of the same review themes. After repeated experiments and optimization in a large-scale of clothing product reviews, the classification accuracy reached up to 93.035% finally. Compared with SVM classification algorithm, the accuracy of maximum entropy in the clothing product reviews classification is higher. This paper verified the effectiveness of maximum entropy model on comment text multi-classification problem, and the maximum entropy model has practical values in electronic business.

scholarly journals Quadratic Support Vector Machine For The Bomba Traditional Textile Motif Classification

2018 ◽  
Vol 11 (3) ◽  
pp. 1004
Author(s):  
Nuraedah Nuraedah ◽  
Muhammad Bakri ◽  
Anita Ahmad Kasim
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Human Life ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Support Vector ◽  
Gray Level ◽  
Textural Feature ◽  
Textile Fabrics

<p><em>The Bomba textile is one of the textile fabrics in Indonesia used in a province called Sulawesi Tengah. Bomba Textile has a unique pattern and has a philosophical meaning in human life in Sulawesi Tengah. Bomba Textile has many motif patterns and varied colors. The problem in this research is the difficulty in classifying every The Bomba textile motif in each class. Data classification is needed to recognize the motif of each Bomba textile pattern and to cluster it into the appropriate class. The features used to classify the Bomba textile motif is the textural feature. Texture features obtained from Gray-Level Co-occurrence matrices (GLCM) method consisting of energy, contrast, homogeneity and correlation with four angles 0</em><em>°</em><em>, 45</em><em>°</em><em>, 90</em><em>°</em><em>, and 135</em><em>°</em><em>. This research will implement Quadratic Vector Machine (QSVM) method with texture feature on Bomba textile pattern. The use of a single texture feature with angles 90</em><em>°</em><em> has an accuracy of 90.3%. The incorporation of texture features by involving all features at all angles can improve the accuracy of the classification model. This research produces a model of motif classification on the Bomba textile which has the classification accuracy of 94.6% and error rate of 5.4%.</em></p>

scholarly journals Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

2020 ◽  
Vol 19 (2) ◽  
pp. 983
Author(s):  
Candra Dewi ◽  
Akbar Grahadhuita ◽  
Lailil Muflikhah
Keyword(s):  
Organic Matter ◽  
Nearest Neighbor ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Occurrence Matrix ◽  
Selection Of ◽  
Color And Texture Features

<span>Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93,33% by using 180 training data, and also particular color and texture feature combination.</span>

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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