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.