Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2019.1691835
A study of a clothing image segmentation method in complex conditions using a features fusion model
Jian Zhang
; School of Art and Design, Changsha University of Science and Technology, Changsha, People’s Republic of China
Caihong Liu
; College of Computer Science, Luohe Vocational Technology College, Luohe, People’s Republic of China
Sažetak
According to a priori knowledge in complex conditions, this paper proposes an unsupervised image segmentation algorithm to be used for clothing images that combines colour and texture features. First, block truncation encoding is used to divide the traditional three-dimensional colour space into a six-dimensional colour space so that more fine colour features can be obtained. Then, a texture feature based on the improved local binary pattern (LBP) algorithm is designed and used to describe the clothing image with the colour features. After that, according to the statistical appearance law of the object region and background information in the clothing image, a bisection method is proposed for the segmentation operation. Since the image is divided into several subimage blocks, bisection image segmentation will be accomplished more efficiently. The experimental results show that the proposed algorithm can quickly and effectively extract effective clothing regions from complex circumstances without any artificial parameters. The proposed clothing image segmentation method will play an important role in computer vision, machine learning applications, pattern recognition and intelligent systems.
Ključne riječi
Clothing image segmentation; block truncation encoding; texture features; unsupervised segmentation
Hrčak ID:
239860
URI
Datum izdavanja:
3.12.2019.
Posjeta: 627 *