Content-based image retrieval using integrated dual deep convolutional neural network

Abstract

The image retrieval focuses on finding images that are similar from a dataset that is of a large scale against an image of a query. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as their shape, colour, and texture. used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deepconvolutional neural networks (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e., learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets of Paris and the oxford dataset considering metrics; also, image retrieval and re-ranking is carried out against the given query. Comparative analysis of various difficulty levels against the different CNN models suggests that IDD-CNN simply outperforms the existing model.