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In support of applications involving multiview sources in distributed objectrecognition using lightweight cameras, we propose a new method for thedistributed coding of sparse sources as visual descriptor histograms extractedfrom multiview images. The problem is challenging due to the computational andenergy constraints at each camera as well as the limitations regardinginter-camera communication. Our approach addresses these challenges byexploiting the sparsity of the visual descriptor histograms as well as theirintra- and inter-camera correlations. Our method couples distributed sourcecoding of the sparse sources with a new joint recovery algorithm thatincorporates multiple side information signals, where prior knowledge (lowquality) of all the sparse sources is initially sent to exploit theircorrelations. Experimental evaluation using the histograms of shift-invariantfeature transform (SIFT) descriptors extracted from multiview images shows thatour method leads to bit-rate saving of up to 43% compared to thestate-of-the-art distributed compressed sensing method with independentencoding of the sources.

Distributed Coding of Multiview Sparse Sources with Joint Recovery