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Trinary-projection trees for approximate nearest neighbor search.

Jingdong Wang1, Naiyan Wang2, You Jia3

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Summary
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We introduce a novel trinary-projection direction for efficient approximate nearest neighbor (ANN) search in visual descriptor indexing. This method enhances data partitioning and speeds up candidate location for large-scale image retrieval.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • Approximate Nearest Neighbor (ANN) search is crucial for large-scale visual descriptor indexing.
  • Existing spatial partition trees (e.g., KD trees, VP trees) use binary partitioning, facing challenges in balancing data grouping and partition function efficiency.
  • Effective ANN search requires efficient data partitioning and rapid candidate location.

Purpose of the Study:

  • To develop a novel partition function for improved approximate nearest neighbor (ANN) search in visual descriptor indexing.
  • To enhance the efficiency and effectiveness of spatial partition trees for large-scale image retrieval tasks.

Main Methods:

  • Designed a trinary-projection direction-based partition function, defining directions using weighted coordinate axes (+1 or -1).
  • Employed the maximum variance criterion to optimize projection directions for robust space partitioning.
  • Developed a coordinate-wise enumeration algorithm to identify the principal trinary-projection direction.
  • Incorporated an extension using multiple randomized trees to further boost performance.

Main Results:

  • The proposed trinary-projection method effectively groups data points, facilitating efficient nearest neighbor candidate location.
  • Fewer coordinate axes in the projection direction ensure faster evaluation of partition functions.
  • Demonstrated approach effectiveness on large-scale local patch indexing and similar image search tasks.
  • The extension with multiple randomized trees showed improved performance.

Conclusions:

  • The trinary-projection direction offers a promising approach for efficient ANN search in visual data.
  • This method improves upon traditional binary space partitioning for large-scale visual indexing.
  • The technique is validated for practical applications like image similarity search.