Hashing Forests for Morphological Search and Retrieval in Neuroscientific Image Databases

Abstract

In this paper, for the first time, we propose a data-driven search and retrieval (hashing) technique for large neuron image databases. The presented method is established upon hashing forests, where multiple unsupervised random trees are used to encode neurons by parsing the neuromorphological feature space into balanced subspaces. We introduce an inverse coding formulation for retrieval of relevant neurons to effectively mitigate the need for pairwise comparisons across the database. Experimental validations show the superiority of our proposed technique over the state-of-the art methods, in terms of precision-recall trade off for a particular code size. This demonstrates the potential of this approach for effective morphology preserving encoding and retrieval in large neuron databases.