An efficient radix trie ‐based semantic visual indexing model for large‐scale image retrieval in cloud environment

The 21st century is the era of big data in the Internet. Online shopping has become a trend, and e-commerce has developed rapidly. With the exponential increase of the amount of commodity image data, the management of massive commodity image database restricts the development of e-commerce to some extent. In order to effectively manage goods and improve the accuracy and efficiency of product image retrieval, this paper uses content-based methods to classify e-commerce images. Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm-AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. By simulating the commodity identification dataset RPC, the results show that the proposed method has obvious advantages in the classification training time and classification accuracy of e-commerce images.

Section: ) Multi-level Clusteringmentioning

confidence: 99%

“…Our living environment is full of vast amounts of information, and people live in the ocean of information. Among the kinds of information that people receive, the most intuitive and most important thing is the image information received through the vision [2]. More than 70% of the human receiving information is received by the visual [3].…”

Section: Introductionmentioning

confidence: 99%

Retracted: Content-Based E-Commerce Image Classification Research

Zhang

2020

“…Second, the joint identification of test samples using HSI color system and image wavelet decomposition [10]. In training and application at the same time, all the images entering the system are stored in the database system [11]. Because the system adopts joint detection technology, when the recognition results of the two classifiers are inconsistent, the supervisor can be invited to judge the final result, so that the intelligent system has the ability of intelligent evolution, and thus the success rate of image recognition of the entire system can be continuously improved [12].…”

Section: Introductionmentioning

confidence: 99%

Retracted: Consumer Marketing Brand Cultivation Path Based on Image Recognition Technology

Liu

Lyu²,

Gao³

2024

In recent years, with the improvement of national income levels, people's consumption concept has begun to transition from enjoyment to quality, and people pay more and more attention to food safety and health. The research of intelligent brand image recognition technology is the most important to prevent the spread of counterfeit and shoddy products and brand cultivation. This article mainly studies the cultivation method of consumer marketing brand based on image recognition technology. This article is based on image recognition technology. Consumers can obtain relevant information and brand culture through the mobile phone camera function anytime, anywhere, establish a bridge between consumers and company brands, broaden the company's channels for obtaining consumer information big data, and deeply tap consumer potential. Demand, further enrich the brand cultivation methods, and achieve a new terminal service experience of "efficient, convenient and satisfactory" for consumers. In the experiment of this paper, the shape of the product packaging is relatively irregular, so there are fewer feature points generated in this part, which leads to the inaccurate extraction and distribution of visual words, which reduces the accuracy of single product extraction slightly, but the overall result is good. The maximum number of iterations in this experiment is 40, which can meet the requirements of most pictures. From the perspective of consumer perception, this paper explores and proposes a new path for consumer marketing brand cultivation of image recognition technology, promotes the deep integration of new artificial intelligence technology and brand industry, and fills the gaps in industry-related technologies and applications.

“…Understand and analyze information, self-regulate and improve awareness of different environments and complex backgrounds, and be able to learn, improve and improve perceived information [2]. With the popularization of image acquisition devices such as smart phones and high-definition cameras [3], combined with the rapid development of VR technology and the Internet, cloud databases receive more than 500 million images every day [4]. How can intelligent robots make full use of the large amount of image information generated by the Internet to improve the target recognition rate Become a hot topic at present.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features

Xing

2020

Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the "one-to-many" rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image.The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.