Efficient Graffiti Image Retrieval
Research of graffiti character recognition and retrieval, as a branch of traditional optical character recognition (OCR), has started to gain attention in recent years. We have investigated the special challenge of the graffiti image retrieval problem and propose a series of novel techniques to overcome the challenges. The proposed bounding box framework locates the character components in the graffiti images to construct meaningful character strings and conduct image-wise and semantic-wise retrieval on the strings rather than the entire image. Using real world data provided by the law enforcement community to the Pacific Northwest National Laboratory, we show that the proposed framework outperforms the traditional image retrieval framework with better retrieval results and improved computational efficiency.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1051200
- Report Number(s):
- PNNL-SA-85319; 400904120; TRN: US201218%%1305
- Resource Relation:
- Conference: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval (ICMR '12), June 5-8, 2012, Hong Kong, China, Article No. 36
- Country of Publication:
- United States
- Language:
- English
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