TY - GEN
T1 - Mobile-based painting photo retrieval using combined features
AU - Companioni-Brito, Claudia
AU - Mariano-Calibjo, Zygred
AU - Elawady, Mohamed
AU - Yildirim, Sule
PY - 2018/6/6
Y1 - 2018/6/6
N2 - 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.
AB - 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.
KW - CBIR
KW - image features
KW - indexing
KW - paintings
KW - similarity
U2 - 10.1007/978-3-319-93000-8_32
DO - 10.1007/978-3-319-93000-8_32
M3 - Conference contribution book
AN - SCOPUS:85049422814
SN - 9783319929996
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 278
EP - 284
BT - Image Analysis and Recognition
A2 - Campilho, Aurelio
A2 - Karray, Fakhri
A2 - ter Haar Romeny, Bart
PB - Springer
CY - Cham, Switzerland
T2 - 15th International Conference on Image Analysis and Recognition, ICIAR 2018
Y2 - 27 June 2018 through 29 June 2018
ER -