Primitive cluster sensitive hashing for scalable content-based image retrieval in remote sensing archives

his paper proposes a novel unsupervised method based on primitive cluster sensitive hashing for fast and accurate image retrieval in large remote sensing (RS) archives. The proposed method consists of a three-steps algorithm. In the first step, each image in the archive is characterized by primitive clusters' descriptors. These descriptors are obtained through an unsupervised approach, which automatically extracts the image regions' descriptors and then associates them with primitive clusters. In the second step the primitive clusters' descriptors are transformed into multi-hash codes to represent each image. Then, in the last step, a multi-hash-code-matching scheme is applied to retrieve the images in the archive that are very similar to a query image. Experiments carried out on an archive of aerial images show that the proposed method provides distinctive multi-hash codes associated to the primitive clusters. Thus, it is more accurate than standard hashing methods, particularly under complex RS image retrieval tasks.