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周亭亭,曹卫群(北京林业大学信息学院, 北京 100083)

摘 要
目的 便捷的商品检索是用户网络购物体验良好的关键环节。由于电商对商品描述方式的规范性要求以及用户对商品属性理解差异等问题,基于关键词的检索方法在商品检索的应用并不理想。近年来,以图搜图的检索方式在各大电商平台上得到越来越多的应用,但检索结果往往不尽如人意。为此,提出了一种新的检索思路,从商品外观设计特征出发,将人们对商品的认知模式引入到商品图片的检索过程,从而获得更符合人们预期的检索结果。方法 以时尚女包商品为例,在分析设计师的设计规范的基础上,将外观设计特征分解为形状特征、颜色特征和设计元素特征。利用深度卷积神经网络建模、提取特征,并使用哈希方法和Top3类内检索算法加快检索速度。结果 利用建立的商品数据集构建3个对应的特征模型,并进行分类识别和图像检索实验。结果表明,各个模型Top1的识别准确率均小于95%,而Top3的识别准确率均在98.5%以上;商品检索速度加快了将近3.5倍。实验及用户调查结果表明,本文提出的检索方法与淘宝、百度图片等基于图像的检索工具相比,检索结果更为多样,与原图像相似度更高。结论 本文提出的从商品外观设计规范出发、与人的认知模式相结合的商品检索方法,更能满足用户的检索意图,可用于时尚女包商品检索,对基于图像的其他商品的检索方法的研究具有借鉴意义。
Commodity retrieval method reflecting people's cognitive habits

Zhou Tingting,Cao Weiqun(School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China)

Objective Rapid and convenient product retrieval is the key for excellent user experience in online shopping. The application of keyword-based retrieval in commodity retrieval is ineffective because of problems, such as standardization of the description of goods and the differences in the understanding of the attributes of the goods by the users. In recent years, "search by image" has been increasingly used in e-commerce platforms. Retrieval technology is constantly improving, from text-based image retrieval to content-based image retrieval, and then to utilizing deep learning to achieve image retrieval. However, retrieval results are often unsatisfactory. These methods cannot rapidly and accurately retrieve results that satisfy people's expectations, thereby lacking excellent user experience. Therefore, a new method of commodity retrieval is proposed. From the features of the commodity design, the image feature is obtained using the complete picture information as well as the human cognition of the goods, which is introduced into the retrieval process of the commodity picture to obtain the desired results. Method Human cognition of commodities is a type of subconsciousness formed by human experience, which corresponds to the designers' norms. We can obtain results that are consistent with human cognitive retrieval results by studying the commodity design specifications and designing commodity features and then using these features for commodity retrieval. We select fashionable women's bags as the research object. Women's bags are a necessity and favorable to women; thus, bags have practical relevance to the study. Moreover, the design elements of women's bags are relatively independent and flexible. Thus, using traditional image retrieval methods is difficult to satisfy user's retrieval intentions. Therefore, studying similar searches of women's bags is necessary. The design features are decomposed into shape, color, and design element features based on the designers' specifications (such as tassel, chain, and zipper). A deep convolution neural network is used to construct classification models for the three features. The features of each picture are then extracted, and three feature sets are established for similarity comparison in retrieval. The shape, color, and design element picture sets are established to construct the feature models that correspond to shape, color, and local design elements, respectively. Each picture set must be marked in advance. The shape picture set is marked by 14 categories, including shell, Boston, and platinum bags. The color picture set is marked by 13 categories, including red, orange, and yellow. The design element picture set is marked by 11 categories, including strip closure, zipper decoration, and diamond grille. Adding a Hashing layer into the deep convolution neural network and extracting Hashing layer data as image features can provide feature binarization and simplify the calculation. At the same time, in the retrieval process, using the proposed Top3 within-class retrieval algorithm can reduce the algorithm complexity. Searching can be according to the classification features, namely, shape, color, and design elements, selected by users in real time. Thus, the retrieval results reflect the users' intention of commodity search. Given a picture of a fashion woman bag image to be retrieved, the corresponding classification model is called after the user selects the classification features. First, the classification of the image under a feature is recognized, and the image feature is then extracted. Subsequently, the Euclidean distance is calculated with all the images in Top3. Finally, the retrieval results are returned in order of similarity. Result The dataset is currently the only one dedicated to the search of fashionable women's bags. Notably, the design element picture set contains not only the overall picture of bags but also the segmented design element picture. The dataset and feature models are used for classification recognition and image retrieval experiments. Results show that the recognition accuracy of each model of the Top1 algorithm is less than 95%, whereas the recognition accuracy of the Top3 is more than 98.5%. Using Top3 within-class retrieval algorithm can speed up the retrieval and ensure the accuracy of the retrieval results as much as possible. At the same time, the use of Hashing method and Top3 within-class retrieval algorithm results in nearly 3.5 times faster retrieval speed and greatly improves the retrieval efficiency. When multiple features for commodity retrieval are used, the corresponding weights of color, shape, and design elements are 0.6, 0.2, and 0.2 respectively. These weights can be defined by the users in real time to reflect the changes of users' attention to different features during the retrieval process. Conclusion A method of commodity retrieval that is based on the commodity appearance design criterion and is combined with people's cognitive model, is proposed. In comparison with image-based retrieval tools, such as Taobao and Baidu, the retrieval results are more similar to the original image and more in line with people's expectations. At the same time, according to the user's preference, the proposed method can synthetically query according to single and multiple features, and the retrieval results are diversified. In addition, we use the global features of shape and color and the local feature of design elements to conduct a survey of online users' retrieval satisfaction. The survey results show that the user satisfaction of Taobao and Baidu pictures is similar. However, the user satisfaction of women's bag retrieval results obtained by the proposed method is remarkably higher than those of Taobao and Baidu pictures, which is more consistent with human cognition. The proposed method is suitable for the retrieval of fashionable women's bags and can be used for reference in the research of image-based retrieval methods for other goods. At present, for a given bag picture, the design elements are obtained by interactive manual segmentation in the process of similar bag retrieval. In future works, we can study the method of identifying the design elements of women's package to realize the automatic identification and segmentation of design elements, thereby improving the automation of women's package retrieval and the practical value of the proposed method.