Query set centered sparse projection learning for set based image classification
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Query set centered sparse projection learning for set based image classification Wenjie Zhu1
· Bo Peng1 · Han Wu1 · Binhao Wang1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Set based image classification technology has been developed successfully in recent decades. Previous approaches dispose set based image classification by employing all the gallery sets to learn metrics or construct the model using a typical number of parameters. However, they are based on the assumption that the global structure is consistent with the local structure, which is rigid in real applications. Additionally, the participation of all gallery sets increases the influence of outliers. This paper conducts this task via sparse projection learning by employing 2,1 norm from the perspective of the query set. Instead of involving all the image sets, this work devotes to searching for a local region, which is centered with a query set and constructed by the candidates selected from different classes in the gallery sets. By maximizing the inter-class while minimizing the intra-class of the candidates from the gallery sets from the query set, this work can learn a discriminate and sparse projection for image set feature extraction. In order to learn the projection, an alternative updating algorithm to solve the optimization problem is proposed and the convergence and complexity are analyzed. Finally, the distance is measured in the discriminate low-dimensional space using Euclidean distance between the central data point of the query set and the central one of images from the same class. The proposed approach learns the projection in the local set centered with the query set with 2,1 norm, which contributes to more discriminative feature. Compared with the existing algorithms, the experiments on the challenging databases demonstrate that the proposed simple yet effective approach obtains the best classification accuracy with comparable time cost. Keywords Query set · Sparse projection learning · Set based image classification · Discriminate subspace learning
1 Introduction Set based image classification, such as face images captured by multiple cameras, long term observations, personal albums, and news pictures, has attracted increasing attention in recent years. Technologies of image classification have been developed greatly in the past years [15, 24, 29, 38, 39]. With the rapid development of digital video techniques, the image sets which are made up of the sequences of videos are simply to be captured and stored for real-time interpretation in video surveillance. Classification of image
Wenjie Zhu
[email protected] 1
Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou, 310018, China
set takes advantageous in the variety of images belonging to the same set, supporting a more complete appearance of this category than one single image. However, it confronts numerous issues along with th
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