IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

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Semantic Image Retrieval in a Grid Computing Environment Using Support Vector Machines

The Computer Journal ◽

10.1093/comjnl/bxt087 ◽

2013 ◽

Vol 57 (2) ◽

pp. 205-216 ◽

Cited By ~ 3

Author(s):

A. Irtaza ◽

M. A. Jaffar ◽

M. T. Mahmood

Keyword(s):

Support Vector Machines ◽

Image Retrieval ◽

Grid Computing ◽

Support Vector ◽

Computing Environment ◽

Semantic Image Retrieval ◽

Vector Machines

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Multi-Level Discrete Cosine Transform for Content-Based Image Retrieval by Support Vector Machines

2007 IEEE International Conference on Image Processing ◽

10.1109/icip.2007.4379510 ◽

2007 ◽

Cited By ~ 3

Author(s):

Yong Li ◽

Xiujuan Chen ◽

Xuezheng Fu ◽

Saeid Belkasim

Keyword(s):

Support Vector Machines ◽

Image Retrieval ◽

Discrete Cosine Transform ◽

Content Based Image Retrieval ◽

Support Vector ◽

Cosine Transform ◽

Vector Machines ◽

Multi Level

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Support Vector Machine for Recognition of White Blood Cells of Leukaemia

Kernel Methods in Bioengineering, Signal and Image Processing ◽

10.4018/978-1-59904-042-4.ch004 ◽

2011 ◽

pp. 93-122 ◽

Cited By ~ 4

Author(s):

Stanislaw Osowski ◽

Tomasz Markiewicz

Keyword(s):

Support Vector Machines ◽

Blood Cells ◽

White Blood Cells ◽

Myelogenous Leukaemia ◽

Image Feature ◽

Gaussian Kernel ◽

Support Vector ◽

Vector Machines ◽

Kernel Support Vector Machines ◽

The Individual

This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow smear. It addresses the following fundamental problems of this task: the extraction of the individual cell image of the smear, generation of different features of the cell, selection of the best features, and final recognition using an efficient classifier network based on support vector machines. The chapter proposes the complete system solving all these problems, beginning from cell extraction using the watershed algorithm; the generation of different features based on texture, geometry, morphology, and the statistical description of the intensity of the image; feature selection using linear support vector machines; and finally classification by applying Gaussian kernel support vector machines. The results of numerical experiments on the recognition of up to 17 classes of blood cells of myelogenous leukaemia have shown that the proposed system is quite accurate and may find practical application in hospitals in the diagnosis of patients suffering from leukaemia.

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