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Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors.

M Eitz, K Hildebrand, T Boubekeur

IEEE Transactions on Visualization and Computer Graphics

|December 22, 2010

Summary

We present a new benchmark for evaluating sketch-based image retrieval systems. Our novel descriptors significantly outperform existing methods, with all data publicly available for research.

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Area of Science:

Computer Vision
Information Retrieval
Machine Learning

Background:

Sketch-based image retrieval (SBIR) systems are crucial for searching large image databases using sketches.
Existing benchmarks may not adequately capture real-world performance for large-scale SBIR.

Purpose of the Study:

To introduce a comprehensive benchmark for evaluating large-scale sketch-based image retrieval systems.
To provide a publicly available dataset and methodology for SBIR research.

Main Methods:

A controlled user study was conducted to collect sketch/image pair matching data.
New descriptors were developed using the bag-of-features approach.
The benchmark was used to evaluate the performance of the developed descriptors.

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Main Results:

The developed bag-of-features descriptors significantly outperformed existing descriptors in SBIR.
The benchmark provides a standardized method for evaluating SBIR system performance.
The dataset and image database are publicly released.

Conclusions:

The introduced benchmark is valuable for advancing sketch-based image retrieval research.
The novel descriptors offer a significant improvement for large-scale SBIR.
Public data availability will foster further development and comparison in the field.