An efficient content based image retrieval framework using separable CNNs

Abstract

Content-Based Image Retrieval (CBIR) is a method for retrieving images based on their content rather than relying on textual descriptions or tags. Over the last decade, Deep Convolutional Neural Networks (D-CNN) based architectures have gained popularity in image retrieval. One major issue with D-CNN is that they are complex, heavy networks with high dimensional features. To overcome this bottleneck, a separable convolutional neural networks based framework has been proposed, which will reduce the complexity of the network. The proposed framework minimizes the length of the final feature vector by extracting the detailed features from the intermediate layers. This step facilitates the avoidance of abstraction of features obtained from the last layer only. The proposed technique has achieved recall and accuracy of the order of 1, indicating a high degree of relevant retrieval. For indexing and similarity matching, the approximate Nearest Neighbour Search (ANNOY) technique has been applied and optimal retrieval has been achieved when compared with the existing techniques, indicating the better performance of the proposed technique. Experimental results indicate that the proposed framework performs exceptionally well compared to the existing state-of-the-art techniques and can be easily employed in various image-related applications.