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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Feature coding in image classification: a comprehensive study.

Yongzhen Huang1, Zifeng Wu1, Liang Wang1

  • 1Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing.

IEEE Transactions on Pattern Analysis and Machine Intelligence
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Summary
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This study surveys feature coding methods in image classification, revealing their evolution through a proposed taxonomy. Empirical evaluations validate theoretical analysis, aiding practical applications and future research in computer vision.

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Area of Science:

  • Computer Vision
  • Pattern Recognition

Background:

  • Feature coding is crucial for image classification, with numerous algorithms developed.
  • Existing studies lack a comprehensive analysis of the connections and evolution of these coding methods.

Purpose of the Study:

  • To survey and analyze various feature coding methods in image classification.
  • To reveal the evolution and relationships between different coding strategies.
  • To provide a taxonomy for understanding the development of feature coding.

Main Methods:

  • Conducted a comprehensive survey of feature coding algorithms, detailing their motivations and mathematical representations.
  • Exploited the relations between coding methods to propose a novel taxonomy.
  • Summarized characteristics shared across different coding strategies.
  • Empirically evaluated representative coding approaches on standard image databases.

Main Results:

  • Established connections and evolutionary paths between diverse feature coding methods.
  • Developed a taxonomy that categorizes and clarifies the progression of these techniques.
  • Experimental results validated the theoretical analysis and highlighted the impact of codebook size and training samples.

Conclusions:

  • The proposed taxonomy offers a structured understanding of feature coding evolution.
  • The findings provide valuable insights for both the practical application and future research directions in image classification.
  • This work bridges the gap in comprehensive studies on feature coding methods.