  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 maximized. The first loss, maximizes the correlation
between the hash centers and learned hash codes. The second loss maximizes the
correlation between the class labels and classification scores. A novel
weighted mean and thresholding based hash center update scheme is proposed for
adapting the hash centers in each epoch. 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 an efficient feature space for image retrieval. The measured mean
average precision shows that the proposed approach outperforms other
state-of-the-art approaches in both single-labeled and multi-labeled image
datasets.
