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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 57880 (02)
- University: Sharif University of Technology
- Department: Mathematical Sciences
- Advisor(s): Mahdavi Amiri, Nezameddin
- Abstract:
- Image classification is a crucial problem in machine learning. Discriminative dictionary learning is recognized as a powerful method for image classification. By incorporating various label information in the dictionary learning process, a dictionary can be generated that reconstructs the original data while focusing on discriminative features. Here, previous dictionary learning-based image classification methods are first reviewed, and then a novel approach, called Deep Discriminative Dictionary Pair Learning, which was introduced by Zhou and colleagues, is explained for image classification. Unlike traditional methods, this approach utilizes deep features extracted from autoencoders as input data. These deep features encompass abstract and high-level information from the data, enhancing discriminative capability. By leveraging the discriminative dictionary learning loss function and the autoencoder loss function, both the potential deep features and their corresponding dictionary pairs can be simultaneously learned. In the testing phase, the minimum error between the deep feature and the image component for different classes is computed. Using this information, the target label is determined through a simple matrix multiplication operation. To compare the performance of the proposed method with existing dictionary learning methods, challenging datasets such as digits and faces are used for numerical experiments
- Keywords:
- Dictionary Learning ; Autoencoder ; Images Classification ; Machine Learning
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