A fast content-based indexing and retrieval technique by the shape information in large image database

Abstract

In this paper, we present an efficient content-based image retrieval (CBIR) system which employs the shape information of images to facilitate the retrieval process. For efficient feature extraction, we extract the shape feature of images automatically using edge detection and wavelet transform which is widely used in digital signal processing and image compression. To facilitate speedy retrieval, we also propose the Spherical Pyramid-Technique (SPY-TEC), a new indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special space partitioning strategy, which is to divide the d-dimensional data space first into 2 d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space. Thus, we are able to use a B +-tree to manage the transformed 1-dimensional data. We show that our image indexing method supports faster retrieval than other multi-dimensional indexing methods such as the R *-tree through various experiments. Finally, we show the retrieval effectiveness of our CBIR system using the measure proposed by the QBIC (C. Faloutsos et al., 1994) system.