Content-based image retrieval based on corel dataset using deep learning

Abstract

A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.

SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning

“…Additionally, Faurina et al [13] showcased the versatility of image analysis techniques, using image captioning for aiding outdoor navigation. Hassan et al [14] addressed content-based image retrieval using deep learning, focusing on the Corel dataset. Zhong et al [15] explored large patch convolutional neural networks for scene classification in high spatial resolution imagery, expanding the application of deep learning models.…”