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Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval.
Gwénolé Quellec1, Mathieu Lamard, Guy Cazuguel
1Institut TELECOM; TELECOM Bretagne, UEB, Department ITI, Brest, F-29200, France. gwenole.quellec@live.com
Summary
This study introduces an adaptive nonseparable wavelet transform for content-based image retrieval (CBIR). The novel method offers superior precision compared to separable wavelet transforms and favorably compares to other advanced techniques.
Area of Science:
- Signal Processing
- Image Analysis
- Computer Vision
Background:
- Multidimensional wavelet filter banks are crucial for signal processing.
- Existing methods often lack adaptability to specific problems.
- Content-based image retrieval (CBIR) requires efficient feature extraction.
Purpose of the Study:
- To develop a novel, adaptive multidimensional wavelet filter bank.
- To enhance the performance of content-based image retrieval (CBIR) systems.
- To provide control over wavelet properties like vanishing moments.
Main Methods:
- Utilizing a nonseparable lifting scheme framework for filter bank design.
- Defining prediction and update filters as Neville filters of specified orders.
- Applying the adaptive nonseparable wavelet transform to generate image signatures for CBIR.
Main Results:
- The proposed system demonstrated notably higher mean precision on three out of four image databases compared to adaptive separable wavelet transforms.
- Performance was comparable to adaptive separable wavelet transforms on the fourth database.
- The method showed favorable comparisons with the dual-tree complex wavelet transform.
Conclusions:
- The novel adaptive nonseparable wavelet transform offers a flexible and effective approach for signal processing tasks.
- This method significantly improves CBIR performance, outperforming existing separable techniques.
- The framework is convenient, applicable across different signal dimensions and lattices.
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