Kernel Coding: General Formulation and Special Cases
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Summary
This work introduces a general coding formulation that englobes the most popular techniques, such as bag of words, sparse coding and locality-based coding, and shows how this formulation and its special cases can be kernelized.
- Type
- preprint
- Published
- 2014-08-29
- Cited by
- 0
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W39368913
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:425710
Keywords
Computer science, Artificial intelligence, Coding (social sciences), Reproducing kernel Hilbert space, Pattern recognition (psychology)
References
- SLEP: Sparse Learning with Efficient Projections
- The Structure of Locally Orderless Images
- Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing
- Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
- Image Classification with the Fisher Vector: Theory and Practice
- Supervised translation-invariant sparse coding
- Term-Weighting Approaches in Automatic Text Retrieval
- Three things everyone should know to improve object retrieval
- Aggregating Local Image Descriptors into Compact Codes
- Evaluation of noise robustness for local binary pattern descriptors in texture classification
- Design of Non-Linear Kernel Dictionaries for Object Recognition
- Recognizing Materials using Perceptually Inspired Features
- Locality-constrained Linear Coding for image classification
- The Pascal Visual Object Classes (VOC) Challenge
- Material perception: What can you see in a brief glance?
- Learning mid-level features for recognition
- Linear spatial pyramid matching using sparse coding for image classification
- Max-Margin Multiple-Instance Dictionary Learning
- The pyramid match kernel: discriminative classification with sets of image features
- Multiple Kernel Learning for Visual Object Recognition: A Review
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