Abstract

No abstract

E-commerce has unmatched advantages over conventional ways of consumption, but it also restricts the amount of contact that customers have with the things they are purchasing. The first step in the transaction product information flow is an effective transaction product display image, which is crucial for product sales. The influence of the emotions present in the current transaction product display image on the buyer's purchase intention has not been established, and as the number of commodity classes rises, there is also a lack of scientific guidance regarding the buyer's efficient retrieval of transaction products. Therefore, this work investigates image classification and retrieval methods for product presentation in e-commerce transactions. A robust network architecture was created for the e-commerce transaction product display and image classification. The image polarity emotion feature extraction backbone module, the polarity emotion intensity perception module, and the emotion feature fusion classification module are the three specific components of the model. The network training approach is an innovation to address the issue that the polarity emotion intensity cannot be effectively conveyed in the e-commerce transaction product display image. The research improves the design of the similarity retrieval algorithm for e-commerce transaction product display images, which increased the retrieval effectiveness for buyers. The correctness of the classification model and retrieval approach was confirmed by the experimental results.

We extend the binary options into barrier binary options and discuss the application of the optimal structure without a smooth-fit condition in the option pricing. We first review the existing work for the knock-in options and present the main results from the literature. Then we show that the price function of a knock-in American binary option can be expressed in terms of the price functions of simple barrier options and American options. For the knockout binary options, the smooth-fit property does not hold when we apply the local time-space formula on curves. By the properties of Brownian motion and convergence theorems, we show how to calculate the expectation of the local time. In the financial analysis, we briefly compare the values of the American and European barrier binary options.

The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.