Abstract

In image retrieval, global features related to color or texture are commonly used to describe the image content. The probleln with this approach is that these global features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed. In this paper we compare a wavelet-based salient point extraction algorithm with two corner detectors using the criteria: repeatability rate and information content. We also show that extracting color and texture information in the locations given by onr salient points provides significantly improved results in terms of retrieval accuracy, computational complexity, and storage space of feature vectors as compared to global feature approaches.

Citations