Image Retrieval Method based on Integration of Principal Component Analysis and Multiple Features
Year: 2019
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 3 : pp. 195–202
Abstract
Jingji Zhao School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, 210044, China (Received May 11 2019, accepted July 20 2019) Existing content-based image retrieval methods exist some drawbacks, such as low retrieval precision, unstable performance. To address these drawbacks, in this paper a content-based image retrieval method is presented based on multi-feature fusion of principal component, oriented-gradient and color histogram. The idea for the proposed method is: firstly, input image is grayscale and flattened into a one- dimensional vector, and the first n principal components from the vector yielded by the PCA algorithm are extracted, in other word, input image is represented as a n×1 dimensional PCA feature vector. Secondly, to remedy color and orientation information missed by PCA, oriented-gradient and color histograms are used to extract orientation and color features respectively. Thirdly, extracted oriented-gradient and color histograms are merged with PCA features to generate the multi-feature representation of the input image. This paper confirms that the proposed multi-feature method can better represent an input image and can easily measure the similarity between images. The experiments are carried out and evaluated based on Corel-1000 , the target method is significantly better than the four popular methods.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22413
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 3 : pp. 195–202
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8