High Accurate and Efficient Image Retrieval Method Using Semantics for Visual Indoor Positioning
- 43 Downloads
Visual indoor positioning has a wide application because of its good positioning performance without additional hardware requirement. However, as the indoor scenes and complexity increase, the offline database will inevitably become large and the online retrieval time will also become long, which make visual indoor positioning unpractical. To solve this problem, we propose a Semantic and Content-Based Image Retrieval (SCBIR) method. By dividing the offline database into semantic databases with different semantic types, the retrieval scope of the image is reduced, and the retrieval time is reduced. First, we use the semantic segmentation method to detect the semantics. Then we divide different semantic scenes in terms of the image order and basic pattern of the semantics in the scene. Finally we use the images belonging to each different semantic scene to build a semantic database, so as to achieve online accurate and fast image retrieval. The experiment results indicate that the proposed method is suitable for large scale retrieval database, and it can reduce the retrieval time in the online stage on the premise of ensuring the accuracy of image retrieval that is critical for visual indoor positioning.
KeywordsVisual positioning Image retrieval Semantic database Database classification
This paper is supported by National Natural Science Foundation of China (61971162, 61771186, 41861134010) and Heilongjiang Province Natural Science Foundation (F2016019).
- 2.Yuda M, Xiangjun Z, Weiming S et al (2017) Target accurate positioning based on the point cloud created by stereo vision. In: International conference on mechatronics & machine vision in practice. IEEEGoogle Scholar
- 3.Parra A, Zhao B, Kim J, et al (2014) Recognition, segmentation and retrieval of gang graffiti images on a mobile device. In: IEEE international conference on technologies for homeland security. IEEE, pp 178–183Google Scholar
- 4.Bouteldja S, Kourgli A (2015) Multiscale texture features for the retrieval of high resolution satellite images. In: International conference on systems, signals and image processing. IEEE, pp 170–173Google Scholar
- 5.Pradhan J, Pal AK, Banka H (2017) A prominent object region detection based approach for CBIR application. In: Fourth international conference on parallel. IEEEGoogle Scholar
- 6.Kido S, Hirano Y, Hashimoto N (2018) Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). In: 2018 international workshop on advanced image technology (IWAIT). IEEEGoogle Scholar
- 7.Xue H, Ma L, Tan X (2016) A fast visual map building method using video stream for visual-based indoor localization. In: International wireless communications and mobile computing conference. IEEE, pp 650–654Google Scholar
- 8.Girshick R (2016) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV). IEEEGoogle Scholar
- 9.Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example miningGoogle Scholar
- 10.Vikhar P, Karde P (2017) Improved CBIR system using Edge Histogram Descriptor (EHD) and Support Vector Machine (SVM). In: International conference on ICT in business industry & government. IEEEGoogle Scholar