Cluster Computing https://doi.org/10.1007/s10586-018-1731-0 Content based image retrieval using deep learning process R. Rani Saritha 1 · Varghese Paul 2 · P. Ganesh Kumar 3 Received: 28 November 2017 / Revised: 26 December 2017 / Accepted: 5 January 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Content-based image retrieval (CBIR) uses image content features to search and retrieve digital images from a large database. A variety of visual feature extraction techniques have been employed to implement the searching purpose. Due to the computation time requirement, some good algorithms are not been used. The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurements. The ultimate aim of the proposed method is to provide an efficient algorithm to deal with the above mentioned problem definition. Here the deep belief network (DBN) method of deep learning is used to extract the features and classification and is an emerging research area, because of the generation of large volume of data. The proposed method is tested through simulation in comparison and the results show a huge positive deviation towards its performance. Keywords Image retrieval · Deep learning · Data analysis · Image extraction 1 Introduction In this era, technology upgrades to its maximum with the help of creativity and innovation, with such an ideas in the field of ANN, the basic module is said to be image processing stream so that most of the systems will map the inputs to its outputs with varied uncertainty logic [1]. The image will be considered as the digital formation and it will be decimated to its corresponding bits. The classification of image or video in the existing systems seems difficult due to its methodology works with the file name search and not the content inside it [2]. Depending upon the query given by the user the ANN should have to classify the content with various attributes. Our proposed algorithm deal with Deep Learning methods, in which it confines each and every data, learns the contents by B R. Rani Saritha ranisaritha3090@gmail.com Varghese Paul vp.itcusat@gmail.com P. Ganesh Kumar ganesh23508@gmail.com 1 Department of Computer Applications, Saintgits College of Engineering, Kottayam, Kerala, India 2 Department of CS/IT, TocH Institute of Science & Techology, Ernakulam, Kerala, India 3 Department of Information Technology, Anna University of Technology, Coimbatore, Tamil Nadu, India separating its features to the deep bottom. The database itself maintains a separate individual data centre that will contain a finite most significant amount of features [3]. Deep learning method shows its maximum performance to its extent and plays a smart extraction of the content from the data, which is on process [4]. Deep learning is one of the classifications of soft comput- ing phenomenon in which extraction of data from millions of segregated images can be retrieved using this phenomenon [5]. The retrieval performance of a content-based image retrieval system crucially depends on the feature repre- sentation and similarity measurement, which have been extensively studied by multimedia researchers for decades Although a variety of techniques have been proposed, it remains one of the most challenging problems in current content-based image retrieval (CBIR) research, which is mainly due to the well-known “semantic gap” issue that exists between low-level image pixels captured by machines and high-level semantic concept perceived by humans. From a high-level perspective, such challenge can be rooted to the fundamental challenge of artificial intelligence (AI) that is, how to build and train intelligent machines like human to tackle real-world tasks [6–8] (Fig. 1). Machine learning is one promising technique that attempts to address this challenge in the long term [9]. Recent years have witnessed some important advanced new techniques in machine learning. Deep learning is the part of machine 123