In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image repre-sentation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth con-straints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correla-tion using two different coding modes: Intra, which exploits the correlation between the elements that compose a de-scriptor; and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task.