Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping

Abstract

In today’s rapid development of network and multimedia technology, the booming of electronic commerce, users in the network shopping species of images and other multimedia information showing geometric growth, in the face of this situation, how to find the images they need in the vast amount of online shopping images has become an urgent problem to solve. This paper is based on the partial differential equation to do the following research: Based on the partial differential equation is a kind of equation that simulates the human visual perception system to analyze images; based on the summary of the advantages and disadvantages of multifeature image retrieval technology, we propose a multifeature image retrieval technology method based on the partial differential equation to alleviate the indexing imbalance caused by the mismatch of multifeature image retrieval technology distribution. To improve the search speed of the data-dependent locally sensitive hashing algorithm, we propose a query pruning algorithm compatible with the proposed partial differential equation-based multifeature image retrieval technology method, which greatly improves the retrieval speed while ensuring the retrieval accuracy; to implement the data-dependent partial differential equation algorithm, we need to distribute the data set among different operation nodes, and to better achieve better parallelization of operations, we need to measure the similarity between categories, and we achieve the problem of distributing data among various categories in each operation node by introducing a clustering method with constraints. The purpose of this article for image recognition is for better shopping platforms for merchants. This algorithm has trained multiple samples and has data support. The experimental results show that our proposed data set allocation method shows significant advantages over the data set allocation method that does not consider category correlation. However, the image features used in image retrieval systems are often hundreds or even thousands of dimensions, and these features are not only high in dimensionality but also huge in number, which makes image retrieval systems encounter an inevitable problem—“dimensionality disaster.” To overcome this problem, scholars have proposed a series of approximate nearest neighbor methods, but multifeature image retrieval techniques based on partial differential equations are more widely used in people’s daily life.

Image Retrieval Using Auto Encoding Features In Deep Learning

“…Starting from basic feature extraction to more advanced techniques in semantic deep learning, the research focused on the key features of various image retrieval and image representation models. Jiaohui Yu [12] widely discussed that the difficulties posed by image retrieval systems are due to the vastness and number of features employed, which can span from hundreds of dimensions to thousands. This phenomenon has been dubbed the dimensionality disaster.…”

“…Real-world circumstances present difficulties in coping with enormous amounts of unlabeled data. The manual labelling procedure is labor-intensive, costly, and necessitates the knowledge of the numbers [12]. Furthermore, supervised feature learning may introduce biases by depending solely on labelled data in addition to being unable to benefit from unlabeled data.…”