Multi-feature fused fabric scanning pattern recognition method

Abstract

The invention discloses a multi-feature fused fabric scanning pattern recognition method. The multi-feature fused fabric scanning pattern recognition method comprises the steps of: filtering yarn textures of a fabric scanning pattern by adopting a texture inhibition fast smooth filtering algorithm, and carrying out gray processing; extracting a main color self-correlation histogram, an edge gradient direction histogram, MSER features and gray-level co-occurrence matrix features, and establishing a sample image feature library; and finally regarding similarities about four types of features among sample images as a training sample, establishing a classifier through adopting an AdaBoost algorithm, and realizing pattern recognition. Therefore, the multi-feature fused fabric scanning pattern recognition method establishes the AdaBoost classifier for fusing the main color self-correlation histogram features, the edge gradient direction histogram features, the MSER features and the gray-level co-occurrence matrix features, thereby achieving automatic adjustment of weight values of various types of features and increasing fabric pattern recognition rate.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a multi-feature fusion fabric scanning pattern recognition method. Background technique [0002] With the improvement of the automation of textile production and design, the management of textile design resources by textile enterprises has changed from traditional manual management to automatic management. Many textile enterprises have accumulated a large number of fabric pictures of different patterns. In order to avoid repeated design and effectively utilize existing resources, an effective method for fabric pattern recognition is urgently needed. [0003] The computer pattern recognition process can generally be divided into three steps: preprocessing, feature extraction, and algorithm recognition. Through image preprocessing, image noise is reduced; then pattern features are extracted and a feature database is established; finally, according to t...

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Problems solved by technology

Bunte et al. proposed an image recognition technology based on color features in a paper titled Learning effective color features for content based image retrieval in dermatology (Pattern Recognition, 2011,44(9):1892-1902); Kekre et al. titled Image Retrieval using Texture Features extracted from GLCM, LBG and KPE (International Journal of Computer Theory and Engineering, 2010, 2(5): 1793-8201) proposes an image recognition technology based on texture features; Kekre et al. titled Image Retrieval with Shape Features Extracted Using Gradient Operators and Slope Magnitude Technique with BTC (International Journal of Computer Applications, 2010, 6(8): 28-33.) The shape feature-based retrieval technology proposed in the article; these single features only reflect the characteristics of a certain aspect of the pattern , cannot fully characterize the pattern features, and pattern recognition using only a certain feature is often not accurate enough

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