TY - GEN
T1 - Identification of orchid species using content-based flower image retrieval
AU - Apriyanti, Diah Harnoni
AU - Arymurthy, Aniati Murni
AU - Handoko, Laksana Tri
PY - 2013
Y1 - 2013
N2 - In this paper, we developed the system for recognizing the orchid species by using the images of flower. We used MSRM (Maximal Similarity based on Region Merging) method for segmenting the flower object from the background and extracting the shape feature such as the distance from the edge to the centroid point of the flower, aspect ratio, roundness, moment invariant, fractal dimension and also extract color feature. We used HSV color feature with ignoring the V value. To retrieve the image, we used Support Vector Machine (SVM) method. Orchid is a unique flower. It has a part of flower called lip (labellum) that distinguishes it from other flowers even from other types of orchids. Thus, in this paper, we proposed to do feature extraction not only on flower region but also on lip (labellum) region. The result shows that our proposed method can increase the accuracy value of content based flower image retrieval for orchid species up to ± 14%. The most dominant feature is Centroid Contour Distance, Moment Invariant and HSV Color. The system accuracy is 85,33% in validation phase and 79,33% in testing phase.
AB - In this paper, we developed the system for recognizing the orchid species by using the images of flower. We used MSRM (Maximal Similarity based on Region Merging) method for segmenting the flower object from the background and extracting the shape feature such as the distance from the edge to the centroid point of the flower, aspect ratio, roundness, moment invariant, fractal dimension and also extract color feature. We used HSV color feature with ignoring the V value. To retrieve the image, we used Support Vector Machine (SVM) method. Orchid is a unique flower. It has a part of flower called lip (labellum) that distinguishes it from other flowers even from other types of orchids. Thus, in this paper, we proposed to do feature extraction not only on flower region but also on lip (labellum) region. The result shows that our proposed method can increase the accuracy value of content based flower image retrieval for orchid species up to ± 14%. The most dominant feature is Centroid Contour Distance, Moment Invariant and HSV Color. The system accuracy is 85,33% in validation phase and 79,33% in testing phase.
KW - HSV color
KW - Support Vector Machine
KW - centroid contour distance
KW - flower image retrieval
KW - orchid
UR - http://www.scopus.com/inward/record.url?scp=84902377406&partnerID=8YFLogxK
U2 - 10.1109/IC3INA.2013.6819148
DO - 10.1109/IC3INA.2013.6819148
M3 - Conference contribution
AN - SCOPUS:84902377406
SN - 9781479910786
T3 - Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013
SP - 53
EP - 57
BT - Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications
PB - IEEE Computer Society
T2 - 2013 International Conference on Computer, Control, Information and Its Applications, IC3INA 2013
Y2 - 19 November 2013 through 21 November 2013
ER -