A Survey on Pattern Recognition Applications of Support Vector Machines

Byun, Hyeran; Lee, Seong Whan

doi:10.1142/s0218001403002460

Cited by 190 publications

(108 citation statements)

References 72 publications

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“…Ref. [9] presents an extensive review of SVM pattern recognition applications. Recently they have been applied to on-line [24] and off-line [23] signature verification problems.…”

Section: Introductionmentioning

confidence: 99%

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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Abstract. -The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification despite its potential applications for accessing security-sensitive facilities and for processing certain legal and historical documents. This paper presents an efficient off-line human signature recognition system based on Support Vector Machines (SVM) and compares its performance with a traditional classification technique, Multi-Layer Perceptrons (MLP). In both cases we propose two approaches to the problem: (1) construct each feature vector using a set of global geometric and moment-based characteristics from each signature and (2) construct the feature vector using the bitmap of the corresponding signature. We also present a mechanism to capture the intrapersonal variability of each user using just one original signature. Our results empirically show that SVM, which achieves up to 71% correct recognition rate, outperforms MLP.

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“…Ref. [9] presents an extensive review of SVM pattern recognition applications. Recently they have been applied to on-line [24] and off-line [23] signature verification problems.…”

Section: Introductionmentioning

confidence: 99%

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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“…The proposed system demonstrates a slightly better performance through these results, but we want to evaluate the C-SVM here. Therefore, we compare the performances of various types of SVMs and SVMs with different kernels, as shown in Figure 7 [17,[20][21][22].…”

Section: Feature Extraction and Ship Detection Resultsmentioning

confidence: 99%

“…A ship is detected by using the feature vector obtained through the above procedure, and the ship detection method uses an SVM as the supervised learning method because of its fast rate of object detection [17][18][19]. The SVM calculates the hyperplane that can maximize the margin between classes and is divided into hard and soft margin techniques.…”

Section: Ship Detectionmentioning

confidence: 99%

Ship Detection Using Edge-Based Segmentation and Histogram of Oriented Gradient with Ship Size Ratio

Eum

Bae

Yoon

et al. 2015

Int. J. Fuzzy Log. Intell. Syst.

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In this paper, a ship detection method is proposed; this method uses edge-based segmentation and histogram of oriented gradient (HOG) with the ship size ratio. The proposed method can prevent a marine collision accident by detecting ships at close range. Furthermore, unlike radar, the method can detect ships that have small size and absorb radio waves because it involves the use of a vision-based system. This system performs three operations. First, the foreground is separated from the background and candidates are detected using Sobel edge detection and morphological operations in the edge-based segmentation part. Second, features are extracted by employing HOG descriptors with the ship size ratio from the detected candidate. Finally, a support vector machine (SVM) verifies whether the candidates are ships. The performance of these methods is demonstrated by comparing their results with the results of other segmentation methods using eight-fold cross validation for the experimental results.

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“…The reason to choose these two classifiers is because k-NN (k = 1) can be used as a benchmark (Jain et al, 2000) and SVM provides better classification performance than many other techniques, such as naïve Bayes, neural networks, decision trees, etc. (Byun and Lee, 2003).…”

Section: Classifier Designmentioning

confidence: 99%

On Classifying Digital Accounting Documents

Цай¹

2007

IJDAR

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Abstract. Advances in computing and multimedia technologies allow many accounting documents to be digitized within little cost for effective storage and access. Moreover, the amount of accounting documents is increasing rapidly, this leads to the need of developing some mechanisms to effectively manage those (semi-structured) digital accounting documents for future accounting information systems (AIS). In general, accounting documents contains such as invoices, purchase orders, checks, photographs, charts, diagrams, etc. As a result, the major functionality of future AIS is to automatically classify digital accounting documents into different categories in an effective manner. The aim of this paper is to examine flat non-hierarchical and hierarchical classification schemes for automatic classification of different types of digital accounting documents. The experimental results show that non-hierarchical classification of digital accounting documents performs better than hierarchically classifying digital accounting documents.

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A Survey on Pattern Recognition Applications of Support Vector Machines

Cited by 190 publications

References 72 publications

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Ship Detection Using Edge-Based Segmentation and Histogram of Oriented Gradient with Ship Size Ratio

On Classifying Digital Accounting Documents

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