Abstract:

Convolutional neural networks  are an established powerful selflearning ability in image recognition tasks. However, supervised deep learning methods are prone to overfitting when the labeled data are small or noisy. To solve these problems, we propose a novel deep selflearning image hashing retrieval method, an unsupervised learning. First, we can obtain a function with discriminative features via unsupervised autoencoding networks, which reduces learning complexity, thus enabling training images not to rely on their semantic labels. The algorithm is, therefore, forced to learn more robust features from the massive unlabeled data. In order to speed up the query, a perceptual hash algorithm is employed. The combination of these two techniques guarantee a better feature description and a faster query speed without depending on labeled data. Experimental results demonstrate that the proposed approach is superior to some of stateofthe-art methods.

Key words: self-learning;perceptual hash algorithm;stacked auto-encoding algorithm;unsupervised learning;image retrieval