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http://hdl.handle.net/1893/31753| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Author(s): | Companioni-Brito, Claudia Mariano-Calibjo, Zygred Elawady, Mohamed Yildirim, Sule |
| Contact Email: | mohamed.elsayed.elawady@stir.ac.uk |
| Title: | Mobile-Based Painting Photo Retrieval Using Combined Features |
| Editor(s): | Karray, Fakhri ter Haar Romeny, Bart Campilho, Aurélio |
| Citation: | Companioni-Brito C, Mariano-Calibjo Z, Elawady M & Yildirim S (2018) Mobile-Based Painting Photo Retrieval Using Combined Features. In: Karray F, ter Haar Romeny B & Campilho A (eds.) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, 10882. ICIAR 2018: International Conference Image Analysis and Recognition, Póvoa de Varzim, Portugal, 27.06.2018-29.06.2018. Cham, Switzerland: Springer International Publishing, pp. 278-284. https://doi.org/10.1007/978-3-319-93000-8_32 |
| Issue Date: | 2018 |
| Date Deposited: | 28-Sep-2020 |
| Series/Report no.: | Lecture Notes in Computer Science, 10882 |
| Conference Name: | ICIAR 2018: International Conference Image Analysis and Recognition |
| Conference Dates: | 2018-06-27 - 2018-06-29 |
| Conference Location: | Póvoa de Varzim, Portugal |
| Abstract: | In paintings or artworks, sharing a photo of a painting using mobile phone is simple and fast. However, searching for information about specific captured photo of an unknown painting takes time and is not easy. No previous developments were introduced in the content-based indexing and retrieval (CBIR) field to ease the inconvenience of knowing the name and other information about an unknown painting through capturing photos by mobile phones. This work introduces an image retrieval framework on art paintings using shape, texture and color properties. With existing state-of-the-art developments, the proposed framework focuses on utilizing a feature combination of: generic Fourier descriptors (GFD), local binary patterns (LBP), Gray-level co-occurrence matrix (GLCM), and HSV histograms. After that, Locality Sensitive Hashing (LSH) method is used for image indexing and retrieval of paintings. The results are validated over a public database of seven different categories. |
| Status: | VoR - Version of Record |
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| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Companioni-Brito2018_Chapter_Mobile-BasedPaintingPhotoRetri.pdf | Fulltext - Published Version | 2.45 MB | Adobe PDF | Under Permanent Embargo Request a copy |
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