A Novel Image Retrieval Technique using Automatic and Interactive Segmentation

Abstract

In this paper, we present a new region-based image retrieval technique based on robust image segmentation. Traditional content-based image retrieval deals with the global description of a query image. We combine the state-of-the-art segmentation algorithms with the traditional approach to narrow the area of interest to a specific region within a query image. In case of automatic segmentation, the algorithm divides a query image automatically and computes Zernike moments for each region. For interactive segmentation, our proposed scheme takes as input a query image and some information regarding the region of interest. The proposed scheme then works by computing the Geodesic-based segmentation of the query image. The segmented image is our region of interest which is then used for computing the Zernike moments. The Euclidean distance is then used to retrieve different relevant images. The experimental results clearly show that the proposed scheme works efficiently and produces excellent results.

Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology

“…Compared with TBIR technology, it saves the time of text annotation and overcomes the subjectivity of manual annotation, thus improving the efficiency and accuracy of retrieval. However, there is a semantic gap between the low-level visual features of images extracted by manual parts and the high-level semantic concepts, which hinders the progress and development of CBIR technology ( Amin & Qureshi, 2020 ).…”