由于电子商务网站的成功发展及网络多媒体技术的迅速普及，在线购物已经成为一种方便、快捷、廉价且时尚的购物方式，但随之而来的是图像数据呈几何级数的增长，对如此超大规模的多媒体数据进行有效管理，并提供迅速、准确的检索服务是一个极具挑战性的课题。目前，电子购物网站的搜索服务仍然依赖基于文本的搜索引擎，标注并关联商品的基本信息，对于用户难以准确地描述的样式、花纹、造型等特有属性缺少进一步的标注，将基于内容的图像自动分类引入电子商务，缓解商品图像数据库的管理压力和提高消费者对商品的检索效率，是当前电子商务领域的迫切需求。
本文以在线购物商品的图像为基础，构建了一个手工标注商品特殊属性的数据集，并以大量实验关注不同的图像特征对商品图像属性的分类检测结果。主要的研究内容和贡献如下：
首先，本文针对原始且粗略的在线商品图像集，从购物用户最关注的色彩和款式两个重要属性出发，基于商品图像特性进行了颜色、纹理和形状分布的剖析，确定运用HSV颜色空间对商品图像提取颜色矩和颜色直方图特征，并采用局部二值模式、梯度局部二值模式、二元梯度轮廓和方向梯度直方图描述纹理信息和形状信息联合表达商品图像的款式属性，通过实验证明了这些特征具有的分类性能。
其次，文中详细介绍了不同底层特征对于商品颜色和款式属性的分类方法细节，对两个属性层面的不同特征进行特征级的融合，构建复合的特征向量并通过实验检验特征组合分类的性能变化，实验结果表明，商品图像的分类准确率得到了部分提升。
最后，虽然每种特征具备特有的分类性能，但不同特征与分类器决策的相关性没有得到综合利用，采用不同内核的分类算法针对特定特征会有突出的表现，因此本文引入了多内核学习方法改进分类决策，设计和运用大量实验测试了颜色、纹理、形状特征联合描述商品图像属性的能力，对比了多组实验的结果并分析了特征在多核学习中的分类性能。

Since the success of e-commerce website development and the rapid popularization of network multimedia technology, online shopping has become a convenient, fast, inexpensive and fashionable way of shopping. However, the ensuing image data is exponentially growing, and effective management of such ultra-large-scale multimedia data as well as providing fast accurate retrieval service is a challenging task. Currently, search services of online shopping are still dependent on text-based search engines by tagging and associated merchandise basic information. Huge problem exists from lack of further annotation for situations that the user finds it difficult to accurately describe the style, pattern, shape and other unique attributes. Introducing content-based image automatic classification to relieve pressure on commodity image database management and improve retrieval efficiency of consumer goods is the current urgent needs of e-commerce.
In this paper, based on merchandise images of online shopping, we build a product dataset with specific attributes manually labeled. Plenty of experiments concerning different characteristics of the product images are carried out for attributes classification. The main contents and contributions are as follows:
Firstly, shopping users mostly concern about two important attributes: color and pattern. According to the color, texture and shape distributes analysis upon the original and rough set of online products images, we extract color moments and color histogram features of the goods images under HSV color space, and choose local binary patterns, gradient-based local binary patterns, binary gradient contours and histogram of oriented gradients to describe the texture and shape information, which can be the representation of pattern attribute. Experiments have proved the classifying performance of these features.
Secondly, the paper describes the classifying detail of different underlying features on color and pattern attributes classification. Feature-level fusion is committed to construct composite feature vectors for the different characteristics of two attributes. Through experimental tests, classification performance changes within different feature combinations. It has shown by experimental results that the accuracy of product image classification has been partly improved.
Lastly, although each feature has its unique classification performance, the associated utilization of different features and cooperated decisions of classifiers have not been taken into consideration. Since using specific kernel in different classification algorithms can facilitate features’ outstanding performance, we introduce the multiple kernel learning methods to improve classification decisions. Certain number of experiments is designed to make best use of color, texture and shape features which collaboratively indicate the product image attributes. By comparing several sets of experimental results, performance analysis of features has been conducted in the classification of multiple kernel learning.