A Semantic Image Retrieval Technique Through Concept Co-occurrence Based Database Organization and DeepLab Segmentation
- 1 University of Calicut, India
- 2 Government Engineering College Thrissur, India
Copyright: © 2020 R. Jayadevan and V.S. Sheeba. 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.
In this paper, a semantic image retrieval technique that efficiently depicts users’ perspective is proposed. It primarily aims in the representation of contextual diversity of the user through a high level semantic segmentation technique called DeepLab-V3+. An online user interactive step is also included during the retrieval process. The significance of intra-concept variation in image retrieval is clearly presented in this paper. An efficient database organization, which forms the essence of the retrieval methodology, based on concept co-occurrence and inter-concept distance is also proposed. ResNet-101 CNN features extracted from the regions are utilized in classification and retrieval tasks. The simulation results and performance analysis conducted on PASCAL VOC2012 and SUN ’09 datasets depict the superiority of the proposed technique over other approaches.
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- Semantic Segmentation
- Concept Co-occurrence
- Intra-concept Variation
- Database Organization
- Contextual Diversity
- Set Formation
- Subset Formation