Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Deterministic constructions of compressed sensing matrices
Journal of Complexity
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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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.