Cloth Pattern Recogntion Using matlab

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                The main objective of this project is to identify and classify the type of dress in an image using an efficient algorithm in machine learning.


Clothing image recognition has recently received considerable attention from many communities, such as multimedia information processing and computer vision, due to its commercial and social applications. However, the large variations in clothing images? appearances and styles and their complicated formation conditions make the problem challenging. Motivated by the fast training and straightforward solutions exhibited by Extreme Learning Machines (ELMs), in this paper, we propose a recognition framework that is based on multiple sources of features and neural networks. In this framework, textural features are first extracted, including DWT. Second, those low-level features are concatenated and taken as the inputs for deep feature-level fusion. By using neural networks, the classification is done.


Existing virtual fitting approaches can be divided into two main groups, 3D model-based, and 2D image-based. There are certain systems that produce results of 3D simulations of the human model and cloth using the physical parameters of the garments. In recent dressing simulations that can reproduce detailed drapes or folds of garments fit on various different body shapes. In the above systems, simulations are done with the help of a predefined human model picture. The customer’s 3D model can be generated using a depth camera. Virtual dressing room solutions work by overlaying the 3d model or picture within the customer’s live video feed. In the video view, the customer can feel the garment or the accessory virtually according to the movement of the customer with the help of a superimposed 3D model [6] or picture. Real 3D simulation fitting rooms have the features of both 3D solutions and photo accurate fitting. With the photo and the simple measurements of the body a 3D figurine is generated, which accurately visualizes customers in chosen apparel items. Another technique for foreground extraction is by complex algorithms such as using grab-cut by obtaining adaptive update tri-map. Another method for contour extraction is the background subtraction by frame to frame subtraction which requires a fixed camera.


  • High complexity
  • Inaccurate results


In the proposed system, cloth classification is done by using neural networks. Here we will be using DWT and GLCM features for feature extraction. For Image segmentation, Lab conversion and K-means clustering are implemented. The description for each of the processes is given as follows,


  1. By using the segmentation approach, we can segment the dress easily
  2. Accurate classification

Block Diagram:



  • MATLAB 2014a and above


[1] H. Chen, A. Gallagher, and B. Girod,?Describing clothing by semantic attributes,? in Proc. of European Conference on Computer Vision (ECCV?12), Firenze, Italy, 2012, pp. 609?623. [Online]. Available:

[2] Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang, ?Deepfashion: Powering robust clothes recognition and retrieval with rich annotations,? in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1096? 1104. [Online]. Available:

[3] S. Yan, Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang, ?Unconstrained fashion landmark detection via hierarchical recurrent transformer networks,? in Proceedings of ACM

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