Research Article Open Access

SEVQER: Automatic Semantic Visual Query Builder to Support Intelligent Image Search in Traffic Images

Hui-Hui Wang1, Phei-Chin Lim1, Yin-Chai Wang1, Soo-See Chai1, Dayang Nurfatimah Awang Iskandar1 and Wee Bui Lin1
  • 1 Universiti Malaysia Sarawak, Malaysia

Abstract

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.

Journal of Computer Science
Volume 14 No. 7, 2018, 1053-1063

DOI: https://doi.org/10.3844/jcssp.2018.1053.1063

Submitted On: 1 February 2018 Published On: 6 August 2018

How to Cite: Wang, H., Lim, P., Wang, Y., Chai, S., Awang Iskandar, D. N. & Lin, W. B. (2018). SEVQER: Automatic Semantic Visual Query Builder to Support Intelligent Image Search in Traffic Images. Journal of Computer Science, 14(7), 1053-1063. https://doi.org/10.3844/jcssp.2018.1053.1063

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Keywords

  • Intention Gap
  • Semantic Visual Query
  • Image Search Interface
  • Semantic-Based Image Retrieval