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Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

W-tree indexing for fast visual word generation.

Miaojing Shi1, Ruixin Xu, Dacheng Tao

  • 1Key Laboratory of Machine Perception (Minister of Education), Peking University, Beijing 100871, China. shimj@cis.pku.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 30, 2012
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Summary
This summary is machine-generated.

This study introduces a novel method to accelerate visual word generation for image retrieval. By leveraging spatial feature correlations and co-occurrence tables, the process significantly speeds up while maintaining accuracy.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • The bag-of-visual-words (BoVW) model is crucial for image retrieval and recognition.
  • Visual word generation, assigning local features to visual words, is computationally intensive.
  • Existing tree-based methods require extensive backtracking for efficiency.

Purpose of the Study:

  • To significantly speed up the visual word generation process in BoVW models.
  • To maintain or improve accuracy during accelerated visual word generation.
  • To reduce the computational cost associated with creating BoVW representations.

Main Methods:

  • Exploiting spatial correlations between local image features.
  • Constructing co-occurrence tables for visual words on large datasets.
  • Assigning probabilistic weights to index structure nodes (e.g., KD-trees) based on co-occurrence.
  • Re-directing search paths to optimize visual word assignment with fewer backtrackings.

Main Results:

  • The proposed scheme demonstrates significant speedup in visual word generation.
  • Accuracy is maintained or improved compared to existing methods.
  • Experimental results validate the efficiency and effectiveness against approximate nearest neighbor libraries and random KD-trees.

Conclusions:

  • The novel approach effectively accelerates visual word generation by utilizing spatial feature relationships.
  • Probabilistic weighting of index structures enhances search efficiency, minimizing backtracking.
  • This method offers a practical solution for large-scale image retrieval and recognition tasks.