RETRACTED: Clothing Style Similarity Matching and Classification Recognition Algorithm Based on Double-Layer Model in the Context of the Internet of Things
This record is marked as retracted by the source. Review the publisher’s notice before using its findings.
Source abstract
Abstract
Abstract Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269995. The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Digital Media and Visual Art
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Apparel and Art Design, Xi’an Polytechnic University 1 , 19 Jinhua South Rd., Beilin District, Xi’an City, Shaanxi Province 710048 ,
School of Apparel and Art Design, Xi’an Polytechnic University 2 , 19 Jinhua South Rd., Beilin District, Xi’an City, Shaanxi Province 710048 ,
References (25)
Show source-provided references
1. Dress with Style: Learning Style from Joint Deep Embedding of Clothing Styles and Body Shapes10.1109/TMM.2020.2980195 ↗
2. View and Clothing Invariant Gait Recognition via 3D Human Semantic Folding10.1109/ACCESS.2020.2997814 ↗
3. Joint Feature and Instance Selection Using Manifold Data Criteria: Application to Image Classification10.1007/s10462-020-09889-4 ↗
4. Converting Video Classification Problem to Image Classification with Global Descriptors and Pre-trained Network10.1049/iet-cvi.2019.0625 ↗
5. Image Classification Method for Tomato Leaf Deficient Nutrient Elements Based on Attention Mechanism and Multi-scale Feature Fusion10.11975/j.issn.1002-6819.2021.17.020 ↗
6. Image Classification Method Based on Iterative Training and Ensemble Learning10.16208/j.issn1000-7024.2020.05.016 ↗
7. Hyperspectral Image Classification of Multi-layer Locality-Aware Convolutional Neural Networks10.3969/j.issn.0490-6756.2020.01.014 ↗
8. Self-Supervised Feature Augmentation for Large Image Object Detection10.1109/TIP.2020.2993403 ↗
9. Visitor-Artwork Network Analysis Using Object Detection with Image-Retrieval Technique10.1016/j.aei.2021.101307 ↗
10. Real-Time Image Enhancement for an Automatic Automobile Accident Detection through CCTV Using Deep Learning10.1007/s00500-021-05576-w ↗
11. A Novel Multi-scale Fusion Framework for Detail-Preserving Low-Light Image Enhancement10.1016/j.ins.2020.09.066 ↗
12. An Image Segmentation Method Based on A Modified Local-Information Weighted Intuitionistic Fuzzy C-Means Clustering and Gold-Panning Algorithm10.1016/j.engappai.2021.104209 ↗
13. Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net10.1007/s10278-020-00410-5 ↗
14. Non-stationary Data Reorganization for Weighted Wind Turbine Icing Monitoring with Gaussian Mixture Model10.1016/j.compchemeng.2021.107241 ↗
15. A Demodulation Method Using a Gaussian Mixture Model for Unsynchronous Optical Camera Communication with On-Off Keying10.1109/JLT.2020.3043046 ↗
16. Probability Density Function of Ocean Noise Based on a Variational Bayesian Gaussian Mixture Model10.1121/10.0000972 ↗
17. Dual Feature Extraction Network for Hyperspectral Image Analysis10.1016/j.patcog.2021.107992 ↗
18. GPU Acceleration of the KAZE Image Feature Extraction Algorithm10.1007/s11554-019-00861-2 ↗
19. Global Statistical Features and Early Scene Interpretation10.1167/5.8.69 ↗
20. Cross-Evaluation of a Parallel Operating SVM - CNN Classifier for Reliable Internal Decision-Making Processes in Composite Inspection10.1016/j.jmsy.2021.07.022 ↗
21. Robust Semi-supervised Support Vector Machines with Laplace Kernel-Induced Correntropy Loss Functions10.1007/s10489-020-01865-3 ↗
22. Development of a Hybrid System Based on Convolutional Neural Networks and Support Vector Machines for Recognition and Tracking Color Changes in Food during Thermal Processing10.1016/j.ces.2021.116679 ↗
23. Asymmetric Possibility and Necessity Regression by Twin-Support Vector Networks10.1109/TFUZZ.2020.3011756 ↗
24. Comparison of the Performance of Partial Least Squares and Support Vector Regressions for Predicting Fatty Acids and Fatty Acid Classes in Marine Oil Dietary Supplements by Using Vibrational Spectroscopic Data10.4315/JFP-19-563 ↗
25. Global Lagrange Stability Analysis of Retarded SICNNs10.1016/j.chaos.2021.110819 ↗