Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing

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.

A Shape-Based Two-Stage Product Image Retrieval Method

Product image retrieval using content of the image is valuable for E-commerce application. But both search efficiency and accuracy are challenging the implementation of content-based image retrieval in large product image database. We present a two-stage product image retrieval method, with fully consideration of individual features of product images. In the initial pruning stage, shape feature based on salient edges of product object is used to generate a moderate number of candidates; in the second stage, the proposed detail feature combined with color and texture features is used for fully retrieval. Experiments show that this two-stage retrieval method accelerates search process with a high accuracy.

Pyramid Histograms of Orientated Gradients for Product Image Retrieval

Traditional text-based image retrieval methods are hard to meet the requirements of on-line product search. This paper applied Content Based Image Retrieval (CBIR) technologies to e-commerce field and designed a product image retrieval algorithm based on Pyramid Histograms of Orientated Gradients (PHOG) descriptor and chi-square distance. By constructing the image retrieval system, we made retrieval tests on PI100 dataset from Microsoft Research Asia. The experimental results proved the efficiency of this algorithm.