Content based Fine-Grained Image Retrieval using Convolutional Neural Network

Content based image retrieval (CBIR) is the problem of retrieving similar images from a database to a given query by use of its visual information only. It has been a hot topic for years. Current CBIR methods rely on the fact that the database consists of large inter-class variance but in real scenario a user for example wants to retrieve same sub-category images to the query, in that case inter-class variance is quite small. Retrieving similar images from database of small inter-class variance is quite difficult from that of large inter-class variance. Convolutional neural networks (CNN) has shown tremendous results in image tasks such as classification, detection, retrieval, segmentation and more. In this paper we proposed a framework for content based fine-grained image retrieval (CB-FGIR) by using CNN. Oxford flower-17 dataset is used to test the proposed framework. Five splits of the dataset is used to evaluate the CB-FGIR framework and achieves superior retrieval results than other handcrafted and state of art methods Keywords— CNN, Retrieval, CB-FGIR, Deep learning.