| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1996: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1995: ¥1,500,000 (Direct Cost: ¥1,500,000)
|
|---|
| Research Abstract |
The aim of this project is to provide a unifying scheme based on wavelet transform for content-based retrieval of image database. Two main components of the scheme are as follows :
1)Hierarchical decomposition of images using orthogonal discrete wavelet transform (DWT) : DWT decomposes an image into three orientation selective detail images and an approximate image. The decomposition process can recursively be applied to the approximate image to produce the next level of the resolution. Thus we can obtain a pyramid structure of subimages with various resolutions corresponding to the different scales. It should be noted that the pyramid structure is the basis for image processing such as edge extraction, texture extraction, texture segmentation, shape extraction, and so on.
2)Feature extraction : We extract features of an image from the subimages obtained by DWT.For example, we use the higher-order local autocorrelation as features for retrieval by example image, shape, user-constructed sketches and drawings. For retrieval by texture, we proposed new features computed from the wavelet coefficients of detailed subimages. These features are stored in the database.
Based on this scheme, we have developed a prototype system of an image database of the historical objects of various places in Japan. This database includes classical pictures, patterns, and instruments, which are Japanese heritages and/or legacy objects collected in the National Museum of Japanese History.
The system allows similarity retrieval based on example images, user-constructed sketches and drawings, textures, and interactive combinations of these. Then the system supports retrieval by browsing which is needed when users are vague about their retrieval needs or unfamiliar with the information available in the database. Experiment with hundreds of images showed good performance.
|
