Texture retrieval using periodically extended and adaptive curvelets
This work addresses texture retrieval for multimedia processing, but it is incremental as it builds on existing curvelet methods with adaptations for specific applications.
The paper tackled texture retrieval by developing two new curvelet-based algorithms suitable for constrained-memory devices, achieving effectiveness on three texture datasets and promising results in seismic activity classification.
Image retrieval is an important problem in the area of multimedia processing. This paper presents two new curvelet-based algorithms for texture retrieval which are suitable for use in constrained-memory devices. The developed algorithms are tested on three publicly available texture datasets: CUReT, Mondial-Marmi, and STex-fabric. Our experiments confirm the effectiveness of the proposed system. Furthermore, a weighted version of the proposed retrieval algorithm is proposed, which is shown to achieve promising results in the classification of seismic activities.