scholarly journals Image Retrieval using Autocorrelation Based Chordiogram Image Descriptor and Support Vector Machine

2019 ◽  
Vol 8 (3) ◽  
pp. 6019-6023 ◽  
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Image Representation ◽  
Haar Wavelet ◽  
Research Area ◽  
Support Vector ◽  
Image Descriptor ◽  
Evolving Systems ◽  
Significant Research ◽  
Benchmark Datasets

Nowadays, evolving systems for indexing and organizing images is more important due to proliferation of images in all domains and it has made content-based image retrieval (CBIR) as significant research area. This paper uses autocorrelation based chordiogram image descriptor (ACID) for effective image representation and Support vector machine (SVM) for effective image classification. The ACID of images is computed from Haar wavelet based multiresolution domain and it exploits shape, texture and geometric details. The proposed combination of ACID and SVM is highly compatible and is comprehensively tested on benchmark datasets namely Gardens Point Walking and St. Lucia and experimental results prove that proposed combination outperforms significantly in terms of precision and recall

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

MULTI-LEVEL DOCUMENT IMAGE SEGMENTATION USING MULTI-LAYER PERCEPTRON AND SUPPORT VECTOR MACHINE

2012 ◽  
Vol 26 (06) ◽  
pp. 1253002 ◽  
Author(s):  
YAN ZHANG ◽  
BIN YU ◽  
HAI-MING GU
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Research Area ◽  
Document Image ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Document Image Analysis ◽  
Important Research Area ◽  
Multi Level ◽  
Document Image Segmentation

Document image segmentation is an important research area of document image analysis which classifies the contents of a document image into a set of text and non-text classes. Previous existing methods are often designed to classify text and halftone therefore they perform poorly in classifying graphics, tables and circuit, etc. In this paper, we present a robust multi-level classification method using multi-layer perceptron (MLP) and support vector machine (SVM) to segment the texts from non-texts and thereafter classify them as tables, graphics and halftones. This method outperforms previously existing methods by overcoming various issues associated with the complexity of document images. Experimental results prove the effectiveness of our proposed method. By virtue of our multi-level classification approach, the text components, halftone components, graphic components and table components are accurately classified respectively which would highly improve OCR accuracy to reduce garbage symbols as well as increase compression ratio thereafter simultaneously.

scholarly journals Recognition of Human Emotion Detection and Annotation, using Local Descriptor and Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 2838-2843
Keyword(s):  
Support Vector Machine ◽  
Object Detection ◽  
Dynamic Environment ◽  
Detection Algorithm ◽  
Research Area ◽  
Multimedia Data ◽  
Video Annotation ◽  
Support Vector ◽  
People Detection ◽  
Local Descriptor

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

Feature selection for damage degree classification of planetary gearboxes using support vector machine

2011 ◽  
Vol 225 (9) ◽  
pp. 2250-2264 ◽  
Author(s):  
J Qu ◽  
Z Liu ◽  
M J Zuo ◽  
H-Z Huang
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Planetary Gear ◽  
Support Vector ◽  
Classification Problems ◽  
Damage Degree ◽  
Binary Model ◽  
Benchmark Datasets ◽  
Degree Classification ◽  
Feature Dimension

Feature selection is an effective way of improving classification, reducing feature dimension, and speeding up computation. This work studies a reported support vector machine (SVM) based method of feature selection. Our results reveal discrepancies in both its feature ranking and feature selection schemes. Modifications are thus made on which our SVM-based method of feature selection is proposed. Using the weighting fusion technique and the one-against-all approach, our binary model has been extensively updated for multi-class classification problems. Three benchmark datasets are employed to demonstrate the performance of the proposed method. The multi-class model of the proposed method is also used for feature selection in planetary gear damage degree classification. The results of all datasets exhibit the consistently effective classification made possible by the proposed method.

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

Sign in / Sign up

Export Citation Format

Share Document