Image based search engine using deep learning

Abstract

During previous couple of years, the World Wide Web (WWW) has become an extremely well-liked information source. To successfully utilize the vast quantity of information that the web provides, we want an effective way to explore it. Image data is much more voluminous than textual data, and visual information cannot be indexed by traditional strategies developed for indexing textual information. Therefore, Content-Based Image Retrieval (CBIR) has received an excellent deal of interest within the research community. A CBIR system operates on the visible features at low-level of a user's input image that makes it troublesome for the users to devise the input and additionally doesn't offer adequate retrieval results. In CBIR system, the study of the useful representation of features and appropriate similarity metrics is extremely necessary for improving the performance of retrieval task. Semantic gap has been the main issue which occurs between image pixels at low-level and semantics at high-level interpreted by humans. Among varied methods, machine learning (ML) has been explored as a feasible way to reduce the semantic gap. Inspired by the current success of deep learning methods for computer vision applications, in this paper, we aim to confront an advance deep learning method, known as Convolutional Neural Network (CNN), for studying feature representations and similarity measures. In this paper, we explored the applications of CNNs towards solving classification and retrieval problems. For retrieval of similar images, we agreed on using transfer learning to apply the GoogleNet deep architecture to our problem. Extracting the last-but-one fully connected layer from the retraining of GoogleNet CNN model served as the feature vectors for each image, computing Euclidean distances between these feature vectors and that of our query image to return the closest matches in the dataset.