Stockman MSU CSE1 Image Database Access  Find images from personal collections  Find images on the web  Find images from medical cases  Find images from art collections  Find images from architectural cases

Stockman MSU CSE2 General methods of query  Use prestored symbolic keys – standard  Use example images provided by user  User specifies colors, textures, shapes  User specifies image regions  User specifies region relationships  User sketches structures of images

Stockman MSU CSE3 Query by example  User provides image (top left)  System creates its own feature rep. to match to other images

Stockman MSU CSE4 QBIC (IBM) color histogram matching; user chooses colors

Stockman MSU CSE5 Query is grid painted by user

Stockman MSU CSE6 Texture features also possible

Stockman MSU CSE7 User can sketch objects (more research needed)  User sketches boundaries of interest  System will use elastic matching (see Ch 14 S&S) on images in DB  Can be expensive

Stockman MSU CSE8 Results of elastic matching

Stockman MSU CSE9 Current problems  Indexing needed for fast browsing, but how can indexes be built?  Computing image features online will be slow, even offline computing will be slow.  What about deeper queries: “show me pictures of children enjoying eating” (same problem faced by traditional databases)  Show me pictures of tragic events, of poverty, of natural beauty, of triumph against bad odds …