Abstract
In the process of small sample object identification, the use of single feature or multi-featured one-dimensional data classification methods has the disadvantages of low accuracy and large amount of sample data. A multi-feature-based small sample recognition method based on DTW algorithm is proposed. Selecting four kinds of object features and using dynamic time warping (DTW) algorithm to regularize the feature of the selected object boundary chain code, to obtain the same dimension of the chain code data for the similarity calculation. Finally, using the Probabilistic Neural Network (PNN) to perform the object recognition training on the four normalized features separately and in different combinations, the features with strong correlations for object type identification are screened out. Compared with K-nearest neighbor algorithm (KNN) recognition results, the method can reduce the data dimension from 11025 to 18, and the recognition accuracy rate is up to 93.9%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
