IEEE Transactions on Pattern Analysis and Machine Intelligence· 2013Q1
Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
- 1,252citations
- Q1SCImago
- 2013year
Short summary
A new Label Consistent K-SVD (LC-KSVD) algorithm learns a discriminative dictionary for sparse coding by enforcing label information on dictionary items, leading to similar sparse codes for features of the same class.
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Key points
- Introduces LC-KSVD algorithm for discriminative dictionary learning in sparse coding.
- Enforces label consistency by associating class labels with dictionary items.
- Combines label consistency, reconstruction, and classification errors into a unified objective function.
- Learns a single overcomplete dictionary and an optimal linear classifier jointly.
- Demonstrates superior performance over recent sparse-coding methods in face, action, scene, and object recognition.
AI-generated from the title and abstract; the full text is not read.
Abstract
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.
The authors' abstract, as published at the source. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 · DOI ↗
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Field: Computational Mechanics
Computational MechanicsEngineering