Product Image Classification Based on Local Features and SVM Classifier

2012 ◽  
Vol 7 (5) ◽  
pp. 1-9 ◽  
Author(s):  
Shijie Jia ◽  
Yuesheng Gu ◽  
Juan Zou
Keyword(s):  
Image Classification ◽  
Local Features ◽  
Svm Classifier ◽  
Product Image

Structure-Aware Deep Learning for Product Image Classification

2019 ◽  
Vol 15 (1s) ◽  
pp. 1-20 ◽  
Author(s):  
Zhineng Chen ◽  
Shanshan Ai ◽  
Caiyan Jia
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Product Image

scholarly journals Effective combining of color and texture descriptors for indoor-outdoor image classification

2014 ◽  
Vol 27 (3) ◽  
pp. 399-410 ◽  
Author(s):  
Stevica Cvetkovic ◽  
Sasa Nikolic ◽  
Slobodan Ilic
Keyword(s):  
Image Classification ◽  
Accurate Method ◽  
Optimal Combination ◽  
Compact Representation ◽  
Svm Classifier ◽  
Classification Methods ◽  
Empirical Tests ◽  
Public Datasets ◽  
Fast Extraction ◽  
Classification Procedures

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors in terms of fast extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets.

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