Objective

2

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

2

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

2

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

2

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.