图像与信号处理  >> Vol. 3 No. 1 (January 2014)

C-SIFT特征结合空间金字塔描述的情感图像分类
Emotional Image Classification Based on Color Scale Invariant Feature Transform Feature and Spatial Pyramid Model

DOI: 10.12677/JISP.2014.31001, PDF, HTML, 下载: 2,538  浏览: 9,484  国家自然科学基金支持

作者: 吕鹏霄*, 顾广华*, 王成儒*, 李扬骏*:燕山大学信息科学与工程学院,秦皇岛

关键词: 图像处理情感分类颜色尺度旋转不变特征空间金字塔局部约束线性编码Image Processing; Emotional Categorization; Colour-Scale Invariant Feature Transform; Spatial Pyramid; Local-Constrained Linear Coding

摘要: 情感图像分类的目的是希望计算机能够表述人类观察图像时所引起的情感反应,并根据这种反应把图像分到不同的情感类别。本文提出了一种基于空间金字塔的情感图像分类方法,首先对图像提取颜色尺度旋转不变特征,并聚类形成视觉特征词典;其次对图像进行空间金字塔分块,使用局部约束线性编码方法表示各子块图像,形成图像的空间金字塔描述;最后通过训练分类器实现对情感图像的分类。该方法在SIFT的基础上加入了具有表征感情色彩的颜色特征,提取了独特的图像情感特征。本文方法分别在国际情绪图片系统(IAPS)数据库和人脸情感数据库(KDEF)上进行了实验,取得了较为理想的情感分类结果。
Abstract: The purpose of emotional image classification is that the computer can express the emotion reaction when observing the image, and classify the images into the different emotional categories automatically. In this paper, we proposed an emotional classification framework based on the spatial pyramid representation. First, we extracted the SIFT (Scale Invariant Feature Transform) feature of the colour, and performed the clustering method to form the codebook. Then, each image is described by using LLC (Local-constrained Linear Coding) scheme, and image representations were performed by the methodology of spatial pyramid. Finally, we performed the emotional categorization by the training classifier. As the colour information is significant to human visual perception we added an extra colour feature. In the experiments made on the IAPS (International Affective Picture System) and KDEF (Karolinska Directed Emotional Faces), an ideal classification result was obtained.

文章引用: 吕鹏霄, 顾广华, 王成儒, 李扬骏. C-SIFT特征结合空间金字塔描述的情感图像分类[J]. 图像与信号处理, 2014, 3(1): 1-8. http://dx.doi.org/10.12677/JISP.2014.31001

参考文献

[1] 高隽, 谢昭 (2009) 图像理解理论与方法. 科学出版社, 北京, 1-30.
[2] 徐思敏 (2012) 基于语义的图像检索关键技术研究. 硕士论文, 沈阳航空航天大学, 沈阳.
[3] Yanulevskaya, V. and van Gemert, J.C. (2008) Emotional valence categorization using holistic image feature. International Conference on Image Processing (ICIP), 101-104.
[4] S. Li, Y.-J. Zhang and H.-C. Tan. (2010) Discovering latent semantic factors for picture categorization. International Conference on Image Processing (ICIP), 1065-1068.
[5] 刘硕研 (2011) 面向感知的图像场景及情感分类算法研究. 硕士论文, 北京交通大学, 北京.
[6] 吴或 (2013) 基于颜色特征的网络不良视频检索技术研究. 成都检测台.
[7] 付赛南 (2013) 基于特征降维的场景分类方法研究. 硕士论文, 上海交通大学, 上海.
[8] Lowe, D. G. (2004) Distinctive Image Features from ScaleInvariant Keypoints. International Journal of Computer Vision, 60, 91-110.
[9] Yang, J., Yu, K., Gong, Y. and Huang, T.S. (2009) Linear spatial pyramid matching using sparse coding for image classification. IEEE Conference on Computer Vision and Pattern Recognition, Miami, 20-25 June 2009, 794-1801.
[10] Yu, K. and Zhang, T. (2010) Improved local coordinate coding using local tangents. Proceedings of the 27th International Conference on Machine Learning, 215-1222.
[11] Wang, J.J., Yang, J.C., Yu, K., et al. (2010) Locality-constrained linear coding for image classification. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 13-18 June 2010, 360-3367.
[12] Lazebnik, S., Schmid, C. and Ponce, J. (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition, New York, 17-22 June 2006, 2169-2178.
[13] Lang, P.J., Bradley, M.M. and Cuthbert, B.N. (1997) International affective picture system (IAPS):Technical manual and affective ratings. NIMH Center for the Study of Emotion and Attention.
[14] Lundqvist, D., Flykt, A. and Öhman, A. (1998) The Karolinska directed emotional faces KDEF. Department of Clinical Neuroscience, Psychology Section, Karolinska Institute.
[15] 李凤彩 (2012) 基于码本模型的场景图像分类研究. 硕士论文, 燕山大学, 秦皇岛.
[16] 涂潇蕾 (2012) 上下文特征结合空间金字塔模型的场景分类算法研究. 硕士论文, 燕山大学, 秦皇岛.