SEVQER: Automatic Semantic Visual Query Builder to Support Intelligent Image Search in Traffic Images
Hui-Hui Wang, Phei-Chin Lim, Yin-Chai Wang, Soo-See Chai, Dayang Nurfatimah Awang Iskandar and Wee Bui Lin
DOI : 10.3844/jcssp.2018.1053.1063
Journal of Computer Science
Volume 14, Issue 7
Image search is a challenging process in the field of Content Based Image Retrieval (CBIR). Image search-by-example, search-by-keyword and search-by-sketch methods seldom provide user interface that allows user to accurately formulate their search intent easily. To overcome such issue, a novel image search interface-Semantic Visual Query Builder (SeVQer) is proposed as a non-verbal interface which allows user to drag and drop from the image data provided to formulate user query. The drag and drop mechanism minimizes the difficulty of verbalizing query image into keywords or sketching a correct drawing of the query image. SeVQer was implemented and compared with 3 image search methods (search-by-example, search-by-keyword and search-by-sketch) in terms of task completion time and user satisfaction using traffic images. SeVQer achieved statistically significant lower task completion time with an average of 28 sec, a promising 50% reduction than search-by-sketch (average of 56 sec). The significance of this work is two-fold: the SeVQer user interface allows user to easily formulate intent specific query, while the novel architecture and methodology reduces the semantic gap in general.
© 2018 Hui-Hui Wang, Phei-Chin Lim, Yin-Chai Wang, Soo-See Chai, Dayang Nurfatimah Awang Iskandar and Wee Bui Lin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.