A novel image retrieval technique using salient edges

The rapidly increasing usage of multimedia environments has led to a greater demand for image retrieval. In this paper, we propose a method for image database retrieval based on salient edges. It achieves both the desired efficiency and accuracy using a three-stage: in the first stage, we extract edge points from the original image and link them to edge curves; in the second stage, we select salient edges according to their lengths, and com*pute rotational angle histogram (RAH) and corners' average frequency (CAF) for every salient edge; in the last stage, a feature vector is generated based on those RAHs and CAFs. We have tested this technique using an image database containing more than 4000 images and all results show that our scheme can perform retrieval efficiently. When an image database is on the order of tens of thousands of images, suitable indexing methods become critical for efficient query processing. This paper also present a new indexing method called tree structured triangle inequality (TSTI), which combines triangle inequality indexing method with tree structured indexing technique. The experiments provide evidence that our proposed method can improve the retrieval speed but not reduce its accuracy.