Deep forest hashing for image retrieval

Abstract

Hashing methods have been intensively studied and widely used in image retrieval. Hashing methods aim to learn a group of hash functions to map original data into compact binary codes and simultaneously preserve some notion of similarity in the Hamming space. The generated binary codes are effective for image retrieval and highly efficient for large-scale data storage. The decision tree is a fast and interpretable model, but the current decision tree based hashing methods have insufficient learning ability due to the use of shallow decision trees. Most current deep hashing methods are based on deep neural networks. However, considering the deficiencies of deep neural network-based hashing, such as the presence of too many hyperparameters, poor interpretability, and requirement for expensive and powerful computational facilities during the training process, a non-deep neural network-based hashing model need to be designed to achieve efficient image retrieval with few hyperparameters, easy theoretical analysis and an efficient training process. The multi-grained cascade forest (gcForest) is a novel deep model that generates a deep forest ensemble classifier to process data layer-by-layer with multi-grained scanning and a cascade forest. To date, gcForest has not been used to generate compact binary codes; therefore, we propose a deep forest-based method for hashing learning that aims to learn shorter binary codes to achieve effective and efficient image retrieval. The experimental results show that the proposed method has better performance with shorter binary codes than other corresponding hashing methods.