Image Retrieval Through Free-Form Query using Intelligent Text Processing
S. A. Angadi1, Hemavati C. Purad2
1S. A. Angadi, Department of Computer Science and Engineering, VTU, Belagavi, (Karnataka), India.
2Hemavati C. Purad, Department of Computer Science and Engineering, VTU, Belagavi, (Karnataka), India.
Manuscript received on 24 May 2023 | Revised Manuscript received on 26May 2023 | Manuscript Accepted on 15 June 2023 | Manuscript published on 30 June 2023 | PP: 40-50 | Volume-12 Issue-7, June 2023 | Retrieval Number: 100.1/ijitee.G96180612723 | DOI: 10.35940/ijitee.G9618.0612723
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image Retrieval is the process of retrieving images from image/multimedia databases. Retrieval of images is performed using various types of queries. A free-form query is a text query that consists of single or multiple keywords, concepts, or descriptions of images, with or without the inclusion of wildcard characters and/or punctuation. This work aims to handle image retrieval based on free-form text queries. Simple and complex queries of conceptual descriptions of images are explored, and an intelligent processing system with free-form queries based on the Bag-of-Words model is modified and built for natural scene images and diverse social images using the Damerau-Levenshtein edit distance measure. The efficacy of the proposed system is evaluated by testing 1,500 free-form text queries, resulting in a recall accuracy of 91.3% on natural scene images (from the Wang/Corel database) and 100% on Diverse Social Images (from the DIV400 dataset). These results demonstrate that the proposed system has achieved satisfactory performance compared to published results, such as the harmonic mean of precision and recall (i.e., F1-Score) of 76.70% and 63.32% at the retrieval of 20 images, as reported in other works.
Keywords: Free-text query; Damerau-Levenshtein edit distance; Bag-of-Words Model
Scope of the Article: Computer Science Applications