A Post Dynamic Clustering Approach for Classification-based Image Retrieval

Content-based Image Retrieval (CBIR) is the process of retrieving images similar to an input query image from a large image dataset. One of the currently trending techniques in this field is classification-based CBIR, which aims to reduce the search space and speed up the final image retrieval. However, owing to the thousands of images in the reduced search space, it takes considerable time to retrieve relevant images. This paper proposes a novel post dynamic clustering-based approach for classification-based CBIR to enhance retrieval accuracy and speed. Initially, a pre-trained CNN architecture is used to predict the class of the input query image and reduce the image search space. Here, clusters of the produced feature space. Next, a semantic cluster sorting technique is suggested to sort all these clusters based on their semantic order. Finally, an optimal subset of these sorted clusters is selected for final image retrieval, which comprises more semantically similar images. The performance of the proposed approach has been tested on five different image datasets. The experimental outcomes confirm that the proposed method is more efficient and faster than competing state-of-the-art schemes.