Search

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that the bag of visual words lacks spatial information in image representation, and unclear description of the image texture, a new method of bag of words model containing spatial information was proposed to identify cashmere wool fibers. The relative position information between local features was incorporated to construct a spatial context synonym table in this method. Combining with soft-assignment, feature points were assigned to several visual words with strong synonymy. The influence of synonymy and ambiguity between words is effectively reduced. Combining the features with spatial information to jointly represent the image texture, it makes up for the defect that the visual word bag model lacks spatial information. The experiment results demonstrate that the average accuracy of this method is improved by 7.9% compared with the traditional bag-of-word, and its average recognition rate can reach 93.3%, which can be used for automatic recognition and classification of cashmere and wool fibers. [ABSTRACT FROM AUTHOR]