Compact storage of correlated data for content based retrieval

Image databases, medical records and geographical information systems contain data that is intrinsically correlated, i.e. elements within a single single record show a high degree of correlation. Content based retrieval is a common technique for querying such databases. The query specifies an image or components that the record is expected to contain or be similar to.We propose a technique for compact storage of such correlated data that is used for content based retrieval . Our method utilizes the machinery of compressive sensing, which allows an under determined system of equations to be approximately solved by l₁-minimization if the data is a sparse linear combination of an appropriate set of basis vectors. Such sparsity is seen in these correlated databases. If the sparsity is high or if some distortion is permitted in the retrieved data, the data can be retrieved by a reconstruction operation with a constant storage cost independent of the number of records stored. If exact retrieval is needed, some additional storage is required for each record, much smaller than the size of the original record. We illustrate the performance of this method with a database of remote sensing images.