Deep Hashing with Hash Center Update for Efficient Image Retrieval

Abstract

In this paper, we propose an approach for learning binary hash codes for image retrieval. Canonical Correlation Analysis (CCA) is used to design two loss functions for training a neural network such that the correlation between the two views to CCA is maximum. The main motivation for using CCA for feature space learning is that dimensionality reduction is possible and short binary codes could be generated. The first loss maximizes the correlation between the hash centers and the learned hash codes. The second loss maximizes the correlation between the class labels and the classification scores. In this paper, a novel weighted mean and thresholding-based hash center update scheme for adapting the hash centers is proposed. The training loss reaches the theoretical lower bound of the proposed loss functions, showing that the correlation coefficients are maximized during training and substantiating the formation of efficient feature space for retrieval. The measured mean average precision shows that the proposed approach outperforms other state-of-the-art methods.