Image Retrieval Based on Locally Features Aggregating

He, Yufeng; Zhou, Luping; Yu, Jiguo; Xu, Tao; Guan, Tao

doi:10.3724/sp.j.1016.2011.02224

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…He et al [18] used the Kansei engineering method to decompose various components of men's shirts, extracted influencing factors of styles, studied the relationship between the style and components, and achieved the style quantification of men's shirts. Ketkar [19] analyzed the modeling factors of dresses and introduced the triangle fuzzy number to fuzzy quantify the relationship between the clothing modeling factors and clothing perceptual words, so as to achieve the quantification of clothing style characteristics.…”

Section: Related Workmentioning

confidence: 99%

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods.

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Section: Related Workmentioning

confidence: 99%