  Nowadays, a huge number of images are available. However, retrieving a
required image for an ordinary user is a challenging task in computer vision
systems. During the past two decades, many types of research have been
introduced to improve the performance of the automatic annotation of images,
which are traditionally focused on content-based image retrieval. Although,
recent research demonstrates that there is a semantic gap between content-based
image retrieval and image semantics understandable by humans. As a result,
existing research in this area has caused to bridge the semantic gap between
low-level image features and high-level semantics. The conventional method of
bridging the semantic gap is through the automatic image annotation (AIA) that
extracts semantic features using machine learning techniques. In this paper, we
propose a novel AIA model based on the deep learning feature extraction method.
The proposed model has three phases, including a feature extractor, a tag
generator, and an image annotator. First, the proposed model extracts
automatically the high and low-level features based on dual-tree continues
wavelet transform (DT-CWT), singular value decomposition, distribution of color
ton, and the deep neural network. Moreover, the tag generator balances the
dictionary of the annotated keywords by a new log-entropy auto-encoder (LEAE)
and then describes these keywords by word embedding. Finally, the annotator
works based on the long-short-term memory (LSTM) network in order to obtain the
importance degree of specific features of the image. The experiments conducted
on two benchmark datasets confirm that the superiority of the proposed model
compared to the previous models in terms of performance criteria.
