A Hybrid Deep Learning Architecture for Latent Topic-based Image Retrieval

Learning effective feature descriptors that bridge the semantic gap between low-level visual features directly extracted from image pixels and the corresponding high-level semantics perceived by humans is a challenging task in image retrieval. This paper proposes a hybrid deep learning architecture (HDLA) that generates sparse latent topic-based representation with the objective of minimizing the semantic gap problem in image retrieval. In fact, HDLA has a deep network structure with a constrained replicated Softmax Model in the lower layer and constrained restricted Boltzmann machines in the upper layers. The advantage of HDLA is that there exist nonnegativity restrictions on the model weights together with ℓ 1 -sparsity enforced over the activations of the hidden layer nodes of the network. This, in turn, enhances the modeling power of the network and leads to sparse, parts-based latent topic representation of images. Experimental results on various benchmark datasets show that the proposed model exhibits better generalization ability and the resulting high-level abstraction yields better retrieval performance as compared to state-of-the-art latent topic-based image representation schemes.