图像分类是计算机视觉的重要研究领域,选择一种特征建立图像间的相似性度量是图像分类的关键问题。鉴于壁画图像自身的特点,轮廓特征是能够表达壁画图像语义的重要特征。研究表明轮廓可以作为图像的重要特征进行图像的识别和分类,但以往研究往往是通过两两最相似轮廓间的chamfer距离来计算图像间的相似性,或者对轮廓建立局部描述符,聚类生成词典,用统计直方图的方式描述图像特征,然后用支持向量机(SVM)进行图像分类。这些方法都忽略了轮廓间的整体结构关系,缺乏对所有轮廓的整体性描述,而现实中一幅图像的语义更多的是一种整体上的语义。基于轮廓整体结构的图像间相似性度量方法,图像间轮廓的相似度计算要受到与其他轮廓空间结构关系的约束,由此生成的相似度更能够表达两幅图像的整体相似程度。实验结果表明本文方法在壁画图像的分类上相对于没有整体结构约束的方法精度有所提高。

Image classification is an important research field of computer vision, and the key problem of which is to select a type of feature and establish the similarity metrics between images. In view of the mural image characteristics, the contour feature plays an important role in expressing the mural image semantics. Many studies have shown that the contours can be used as an important feature in image recognition and classification. However, previous studies tend to use the chamfer distance between each pair of the most similar contours to compute the similarity between images, or build local descriptors for each contour, clustering into codebook, and describe the image features as histograms, then do the classification using SVM. However, these methods ignore the overall structure between the contours, lack of the overall view of the all contours, while in reality the semantics of an image tend to be more of a holistic semantic. In this paper, we study the similarity metrics between images based on the overall structure of contours, the calculation of contour similarity is subject to the constraint of the space structure relations with other contours, the generated similarity are more able to express the overall similarity between two images. The experimental results show that our method improved accuracy compared to others in mural image classification.