Convolution Autoencoder based Classification Model for Content Based Image Retrieval

The Content Based Image Retrieval (CBIR) has gained significant importance due to surge of online images and its diverse applications. The CBIR system gets an image as input and returns the most analogous and pertinent image from the database. The CBIR has a wide variety of applications ranging from crime prevention to medical diagnosis and an efficient CBIR system may further improve these areas. In CBIR systems images are converted into binary codes for search and retrieval. There are many approaches for converting images into binary codes but most of them could not properly represent the non-linear relationship exist within the images. To overcome this issue many deep learning architectures and models have been proposed for learning the non-linear relationship within an image. The models used may be unsupervised learning models such as autoencoders or supervised learning models like Convolutional Neural Networks (CNN) with their respective advantages and limitations. Therefore, this paper proposes a convolutional autoencoder model for exploiting the benefits of autoencoder and CNN models for CBIR system. In this way both the data and the class distribution are considered while producing the binary code for CBIR system. The proposed model is evaluated on CIFAR-10 training dataset considering ten classes and performance is also compared with the other existing models. The convolutional autoencoder model has produced better bit codes with more than 95% accuracy as compared to an autoencoder model and has outperformed state of the state of art autoencoder models used for CBIR.