|Table of Contents|

Chinese Painting Emotion Classification Based onConvolution Neural Network and SVM(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年03期
Page:
74-
Research Field:
·计算机科学·
Publishing date:

Info

Title:
Chinese Painting Emotion Classification Based onConvolution Neural Network and SVM
Author(s):
Wang Zheng1; Li Haoyue1; Xu Hongshan1; Sun Meijun2
(1.Laboratory for Big Data Analysis and System(School of Software,Tianjin University),Tianjin 300350,China)(2.School of Computer Science and Technology,Tianjin University,Tianjin 300350,China)
Keywords:
image emotion; Chinese painting; convolution neural network; feature extraction; support vector machine
PACS:
K879.4,TP183
DOI:
10.3969/j.issn.1001-4616.2017.03.011
Abstract:
Image emotions are human emotional responses caused by the contents of digital images. Computers are able to classify different images according to different human emotional responses. With the rapid growth of the amount of information,image emotion classification will contribute to the image annotation and search producing great social and commercial value. Chinese paintings have obvious characteristics:traditional Chinese paintings do not focus on the perspective,and do not emphasize the light color changes of objects in nature,and do not rigidly adhere to the appearance of objects. They more focus on the expression of authors’ subjective consciousness making it harder to bridge the semantic gap between general low-level features and human emotions. The structure of convolutional neural network(CNN)is simple,yet its adaptability is strong. CNN also has less training parameters and more junctions,and are able to read images directly without preprocessing images complexly. It has a huge advantage over traditional image-processing method. This paper aims to explore the relationships between low-level features and emotional semantics by CNN,and extract the features of Chinese paintings and process the features by PCA and normalization. Finally we classify the features by SVM.

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Last Update: 2017-09-30