摘要
图像语义分类在基于语义的图像检索中具有重要意义,但是图像的情感语义描述和分类方面的研究在近年来才刚刚起步。该文利用图像的低层特征实现了图像高层情感语义(“静感”和“动感”)的分类。图像的线条与情感之间存在明显的联系,选用线条方向直方图作为图像特征,利用概率神经网络(PNN)完成语义分类,实验表明该方法具有较好的效果。
Image semantic classification using low-level features is an important problem in semantic-based image retrieval. Although semantic description and classification in emotional way have become remarkable in recent years, the study in this field is still at the very beginning. This paper shows how high-level emotional representation of art paintings can be inferred from perceptual level features suited for the particular classes (dynamic vs. static classification). According to the strong relationship between notable lines of image and human sensations, the edge-based line direction histogram is selected as the image feature, and probabilistic neural network (PNN) is used to establish the mapping between image feature and semantic description, then images can be classified into dynamic vs. static. Experimental results demonstrate the effectiveness of the approach.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2005年第11期7-9,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60372068)
关键词
图像语义分类
情感语义
线条方向直方图
神经网络
图像动感
Image semantic classification
Emotional semantic
Line direction histogram
Neural networks
Dynamic sensation
