9 April 1997 Approach to clustering large visual databases using wavelet transform
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Many applications demand the capability of retrieval based on image content. A classification mechanism is needed to categorize images based on feature similarity. An effective classification of the images can support efficient retrieval of images. In this paper, we investigate a feature-based approach to image clustering and retrieval. Four different texture-based feature sets of images are extracted using Haar and Daubechies wavelet transforms. Using multi- resolution property of wavelets, we extract the features at different levels. The experimental results of our clustering approach on air photo images are reported.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gholamhosein Sheikholeslami, Aidong Zhang, "Approach to clustering large visual databases using wavelet transform", Proc. SPIE 3017, Visual Data Exploration and Analysis IV, (9 April 1997); doi: 10.1117/12.270327; https://doi.org/10.1117/12.270327

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