Text Localisation for Roman Words from Shop Signage

Text localisation determines the location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Results indicate that HOG with quadratic kernel function localises text for shop signage better than SURF.

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Leaf Recognition using Texture Features for Herbal Plant Identification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v9.i1.pp152-156 ◽

2018 ◽

Vol 9 (1) ◽

pp. 152 ◽

Cited By ~ 6

Author(s):

Zaidah Ibrahim ◽

Nurbaity Sabri ◽

Nur Nabilah Abu Mangshor

Keyword(s):

Recognition Performance ◽

Texture Features ◽

Support Vector ◽

Svm Classifier ◽

Plant Identification ◽

Useful Knowledge ◽

Speeded Up Robust Features ◽

Herbal Plants ◽

Multiclass Support Vector Machine ◽

Herbal Plant

This research investigates the application of texture features for leaf recognition for herbal plant identification. Malaysia is rich with herbal plants but not many people can identify them and know about their uses. Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary. Leaf image is chosen for plant recognition since it is available and visible all the time. Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections. A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier. A new leaf dataset has been constructed from ten different herb plants. Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.

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Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

BioMed Research International ◽

10.1155/2016/4783801 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zhu-Hong You ◽

Yu-Hong Fang ◽

Yu-Jun Zhao ◽

...

Keyword(s):

Protein Sequences ◽

Relevance Vector Machine ◽

Experimental Results ◽

Computational Method ◽

Support Vector ◽

Svm Classifier ◽

Local Phase ◽

Local Phase Quantization ◽

Phase Quantization ◽

Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

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A Kernel-Based Approach for Biomedical Named Entity Recognition

The Scientific World JOURNAL ◽

10.1155/2013/950796 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Rakesh Patra ◽

Sujan Kumar Saha

Keyword(s):

Kernel Function ◽

Text Processing ◽

Named Entity Recognition ◽

Kernel Functions ◽

Entity Recognition ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Named Entity ◽

Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

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A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-281-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 281-285 ◽

Cited By ~ 21

Author(s):

B. Yekkehkhany ◽

A. Safari ◽

S. Homayouni ◽

M. Hasanlou

Keyword(s):

Kernel Function ◽

Temporal Integration ◽

Experimental Tests ◽

Kernel Functions ◽

Polynomial Kernel ◽

Support Vector ◽

Svm Classifier ◽

Rbf Kernel ◽

Multi Temporal ◽

Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

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Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020109 ◽

2020 ◽

Vol 9 (2) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Bo Cheng ◽

Shiai Cui ◽

Xiaoxiao Ma ◽

Chenbin Liang

Keyword(s):

High Resolution ◽

Extraction Method ◽

Texture Features ◽

Decomposition Methods ◽

Projection Matrix ◽

Training Data ◽

Support Vector ◽

Svm Classifier ◽

Building Area ◽

Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

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Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽

10.3390/electronics9091443 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1443

Author(s):

Mai Ramadan Ibraheem ◽

Shaker El-Sappagh ◽

Tamer Abuhmed ◽

Mohammed Elmogy

Keyword(s):

Area Under The Curve ◽

Malignant Neoplasm ◽

Texture Features ◽

Feature Space ◽

Skin Neoplasms ◽

Support Vector ◽

Svm Classifier ◽

Abnormal Growth ◽

Malignant Skin ◽

High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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Content-based image retrieval based on supervised learning and statistical-based moments

Modern Physics Letters B ◽

10.1142/s0217984919502130 ◽

2019 ◽

Vol 33 (19) ◽

pp. 1950213 ◽

Cited By ~ 9

Author(s):

Vibhav Prakash Singh ◽

Rajeev Srivastava ◽

Yadunath Pathak ◽

Shailendra Tiwari ◽

Kuldeep Kaur

Keyword(s):

Image Retrieval ◽

Supervised Learning ◽

Texture Features ◽

Ground Truth ◽

Local Binary Patterns ◽

Content Based Image Retrieval ◽

Support Vector ◽

Svm Classifier ◽

Learning Approaches ◽

Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

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A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12.16171 ◽

2018 ◽

Vol 7 (3.12) ◽

pp. 521 ◽

Cited By ~ 1

Author(s):

Pathanjali C ◽

Vimuktha E Salis ◽

Jalaja G ◽

Latha A

Keyword(s):

Comparative Study ◽

Detection System ◽

Experimental Result ◽

Support Vector ◽

Svm Classifier ◽

Nearest Neighbour ◽

Classification Models ◽

Food Detection ◽

Knn Classifier ◽

Indian Food

Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier.

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Classification for Liver Diseases Based on Ultrasound Image Texture Features

10.20944/preprints201810.0073.v1 ◽

2018 ◽

Author(s):

Sendren Sheng-Dong Xu ◽

Chien-Tien Su ◽

Chun-Chao Chang ◽

Pham Quoc Phu

Keyword(s):

Liver Abscess ◽

Liver Diseases ◽

Ultrasound Image ◽

Texture Features ◽

Image Texture ◽

Support Vector ◽

Svm Classifier ◽

Ultrasound Images ◽

Gray Level ◽

Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

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