Qi Tian Univ. of Illinois/Urbana-Champaign (United States) Nicu Sebe,Michael S. Lew Leiden Institute of Advanced Computer Science (Netherlands) E. Loupias INSA-Lyon (France) Thomas S. Huang Univ. of Illinois/Urbana-Champaign (United States)
Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Most of the attention from the research has been focused on indexing techniques based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. The use of interest points in content-based image retrieval allow image index to represent local properties of the image. Classic corner detectors can be used for this purpose. However, they have drawbacks when applied to various natural images for image retrieval, because visual features need not be corners and corners may gather in small regions. In this paper, we present a salient point detector. The detector is based on wavelet transform to detect global variations as well as local ones. The wavelet-based salient points are evaluated for image retrieval with a retrieval system using color and texture features. The results show that salient points with Gabor feature perform better than the other point detectors from the literature and the randomly chosen points. Significant improvements are achieved in terms of retrieval accuracy, computational complexity when compared to the global feature approaches.