Improved Multiple Vector Representations of Images and Robust Dictionary Learning

Chengchang Pan,Zhongwei Cui,Zewei Wang,Yongjun Zhang

doi:10.3390/electronics11060847

Chengchang Pan, Zhongwei Cui + Show 2 more

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https://doi.org/10.3390/electronics11060847

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Abstract

Each sparse representation classifier has different classification accuracy for different samples. It is difficult to achieve good performance with a single feature classification model. In order to balance the large-scale information and global features of images, a robust dictionary learning method based on image multi-vector representation is proposed in this paper. First, this proposed method generates a reasonable virtual image for the original image and obtains the multi-vector representation of all images. Second, the same dictionary learning algorithm is used for each vector representation to obtain multiple sets of image features. The proposed multi-vector representation can provide a good global understanding of the whole image contour and increase the content of dictionary learning. Last, the weighted fusion algorithm is used to classify the test samples. The introduction of influencing factors and the automatic adjustment of the weights of each classifier in the final decision results have a significant indigenous effect on better extracting image features. The study conducted experiments on the proposed algorithm on a number of widely used image databases. A large number of experimental results show that it effectively improves the accuracy of image classification. At the same time, to fully dig and exploit possible representation diversity might be a better way to lead to potential various appearances and high classification accuracy concerning the image.

Highlights

Image classification has always been one of the important research topics in the field of computer vision
In order to speed up the sparse decomposition process, there are generally two ways: One is to reduce the operation scale by selecting training samples with certain properties [11–13], and the other is to obtain a more compact dictionary containing a large amount of identification information through DL, such as the classical K-Singular Value Decomposition algorithm (KSVD) [7], Discriminative K-SVD (D-KSVD) [14], Label Consistent K-SVD (LC-KSVD) [15] and fisher discrimination dictionary learning (FDDL) [16]
The original include algorithm is represented as improvement todatabase, K-SVD (IKSVD), databases.[21]

Summary

Introduction

Image classification has always been one of the important research topics in the field of computer vision. Researchers have generally used sparse representation (SR) and dictionary learning (DL) to extract image features. These methods have a good learning ability for image representation and can show better recognition results in image classification tasks [7–9]. In order to speed up the sparse decomposition process, there are generally two ways: One is to reduce the operation scale by selecting training samples with certain properties (such as neighbor samples) [11–13], and the other is to obtain a more compact dictionary containing a large amount of identification information through DL, such as the classical K-Singular Value Decomposition algorithm (KSVD) [7], Discriminative K-SVD (D-KSVD) [14], Label Consistent K-SVD (LC-KSVD) [15] and fisher discrimination dictionary learning (FDDL) [16]. The novel research field successfully combines machine learning and swarm intelligence approaches and proved to be able to obtain outstanding results in different areas [17,18]

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Results

Conclusion