Automatic product image classification with multiple support vector machine classifiers

2011 ◽  
Vol 16 (4) ◽  
pp. 391-394 ◽  
Author(s):  
Shi-jie Jia ◽  
Xiang-wei Kong ◽  
Hong Man
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Product Image ◽  
Multiple Support ◽  
Automatic Product

Product-Image Classification with Support Vector Machine

2012 ◽  
Vol 433-440 ◽  
pp. 6019-6022
Author(s):  
Shi Jie Jia ◽  
Jian Ying Zhao ◽  
Yan Ping Yang ◽  
Nan Xiao
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Kernel Functions ◽  
Experimental Results ◽  
Support Vector ◽  
Chi Square ◽  
Image Dataset ◽  
Histogram Intersection ◽  
Product Image ◽  
Spatial Pyramid

SVMs with kernel have been established with good generalization capabilities. This paper proposed a supervised product-image classification method based on SVM and Pyramid Histogram of words(PHOW). We tested several kernel functions on PI100 (Microsoft product-image dataset), such as linear, Radial Basis, Chi-square, histogram intersection and spatial pyramid kernel. Experimental results showed the effectiveness of our algorithm.

A Novel Technique for Subpixel Image Classification Based on Support Vector Machine

2010 ◽  
Vol 19 (11) ◽  
pp. 2983-2999 ◽  
Author(s):  
Francesca Bovolo ◽  
Lorenzo Bruzzone ◽  
Lorenzo Carlin
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Novel Technique

SAR Image Classification by Support Vector Machine

2007 ◽  
pp. 341-353
Author(s):  
Toru Fujinaka ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Sar Image

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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