Nowadays, local feature descriptors have emerged as one of the most promising and powerful visual representation solutions. In fact, with a minimal amount of computational effort, the detection and extraction of visual features can provide reliable and a compact image representation that enables a rich set of image analysis tasks. In this paper, a visual sensor network scenario is considered with energy and bandwidth constraints at each sensor node. In this scenario, low-level binary features can be computed with low complexity and efficient coding schemes can reduce the data rate needed to transmit the features. This paper proposes two binary descriptor coding techniques that exploit the correlation between descriptors of the same image by clustering the extracted descriptors with two solutions: divisive-clustering and agglomerative-clustering. While the former starts with one cluster containing all descriptors which is recursively divided, the latter starts with as many clusters as descriptors which are recursively grouped. The disjoint sets of descriptors are differentially coded and the prediction residual is entropy coded. The experimental results show bitrate savings up to 34% without any impact in the accuracy of the final image retrieval task.