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01-07-2019 | REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES | Issue 3/2019

Pattern Recognition and Image Analysis 3/2019

Image Classification Model Using Visual Bag of Semantic Words

Journal:
Pattern Recognition and Image Analysis > Issue 3/2019
Authors:
Yali Qi, Guoshan Zhang, Yeli Li
Important notes
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819030222/MediaObjects/11493_2019_6008_Fig15_HTML.gif
Yali Qi received his BS and MS degrees in computer application from the Petroleum University of China in 1999 and 2003, respectively, and her PhD degree from the Tianjin University in 2017. Now she works as an associate professor in Department of Computer Science, Beijing Institute of Graphic Communication. Her current research interests include image semantic analysis and machine learning.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819030222/MediaObjects/11493_2019_6008_Fig16_HTML.gif
Guoshan Zhang received PhD degree in School of Electrical Engineering and Automation, Northeastern University. Now he works as a professor in School of Electrical and Information Engineering, Tianjin University. The research interesting are nonlinear system control, intelligent control, and engineering application.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819030222/MediaObjects/11493_2019_6008_Fig17_HTML.gif
Yeli Li received PhD degree in School of Electrical Engineering and Automation, Northeastern University. Now she works as a professor in Department of Computer Science, Beijing Institute of Graphic Communication. The research interesting are image process, embedded system.

Abstract

In the image classification field, the visual bag of words (BoW) has two drawbacks. One is low classification accuracy because a visual BoW is typically extracted from local low-level visual feature vectors via key points, without considering the high-level semantics of an image. The other is excessive time consumption because the size of the vocabulary is very large, especially for images with explicit backgrounds and object content. To solve these two problems, we propose a novel image classification model based on a visual bag of semantic words (BoSW), which includes an automatic segmentation algorithm based on graph cuts to extract major semantic regions and a semantic annotation algorithm based on support vector machine to label the regions with a visual semantic vocabulary. The proposed BoSW model refines image semantics by introducing user conceptions for extracting semantic vocabularies and reducing the size of the vocabulary. Experimental results demonstrate the superiority of the proposed algorithm through comparisons with state-of-the-art methods on benchmark datasets.

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Literature
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