Efficient content-based image retrieval in digital picture collections using projections: (near)-copy location

Digital storage of large photo collections opens the way to computer-aided queries based on visual rather than thematic search patterns. The objective of our queries in this research was the 19th-century mass-produced studio portrait or carte-de-visite, whose front and back sides provide a testbed for gray level and binary images classes. We established a ground truth for detecting highly similar images (former copies) in different classes of B/W images. The results will serve as a reference benchmark for yet to be developed visual search methods. The similarity measure used for locating near-copies was the average distance in pixel intensity for shifted image pairs with normalized position, orientation, resolution and lighting. To measure the performance of possible hierarchical comparison and ranking protocols, we scanned in test sets of known copies and near-copies together with over a thousand similar format pictures. The results show that projections are highly effective and efficient in indexing raw image data: reduced dimensionality, low noise, conservation of pattern characteristics, separable x- and y-translation (best shift) and suitable for hierarchical indexing. A query by example WWW demo program with precalculated ranking result files was developed for visual inspection and evaluation of similar image location methods