A bag of constrained informative deep visual words for image retrieval

In this paper, we propose a bag of constrained informative deep visual words (BoCIDVW) model for image retrieval. Informative patches from each image are first obtained using patch entropy values. Each such patch is represented by deep features extracted through VGG16-Net. Two sets of constraints, namely, the must-link (ML) and the cannot-link (CL), are obtained for each deep informative patch in an unsupervised manner from its mutual information values (with other patches). The patches are then quantized using the Linear-time Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained K-means algorithm. The resulting clusters, which we term constrained informative deep visual words, are employed to label each patch. Finally, a bag (histogram) of constrained informative visual words is developed for image retrieval. Experiments on three different publicly available datasets demonstrate the merit of the proposed formulation. (C) 2019 Elsevier B.V. All rights reserved.