Geometric min-Hashing: Finding a (Thick) Needle in a Haystack

Ondra Chum
(CMP Prague, Czech Republic,

joint work with Michal Perdoch and Jiri Matas


We propose a novel hashing scheme for image retrieval, clustering and automatic object discovery. Unlike commonly used bag-of-words approaches, the spatial extent of image features is exploited in our method. The geometric information is used both to construct repeatable hash keys and to increase the discriminability of the description. Each hash key combines visual appearance (visual words) with semi local geometric information.

Compared with the state-of-the-art min-Hash, the proposed method has both higher recall (probability of collision for hashes on the same object) and lower false positive We demonstrate the power of the proposed method on small object discovery in a large unordered collection of images and on a large scale image clustering problem.