  In support of applications involving multiview sources in distributed object
recognition using lightweight cameras, we propose a new method for the
distributed coding of sparse sources as visual descriptor histograms extracted
from multiview images. The problem is challenging due to the computational and
energy constraints at each camera as well as the limitations regarding
inter-camera communication. Our approach addresses these challenges by
exploiting the sparsity of the visual descriptor histograms as well as their
intra- and inter-camera correlations. Our method couples distributed source
coding of the sparse sources with a new joint recovery algorithm that
incorporates multiple side information signals, where prior knowledge (low
quality) of all the sparse sources is initially sent to exploit their
correlations. Experimental evaluation using the histograms of shift-invariant
feature transform (SIFT) descriptors extracted from multiview images shows that
our method leads to bit-rate saving of up to 43% compared to the
state-of-the-art distributed compressed sensing method with independent
encoding of the sources.
