Browsing by Author "Tirkaz,C."

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Citation Count: 7
Sabanci-Okan System at Image Clef 2011: Plant identification task
(CEUR-WS, 2011) Yanikoglu,B.; Aptoula,E.; Tirkaz,C.
We describe our participation in the plant identification task of Image Clef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our first attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our efforts.
Citation Count: 12
Sabanci-okan system at image Clef 2012: Combining features and classifiers for plant identification
(CEUR-WS, 2012) Yanikoglu,B.; Aptoula,E.; Tirkaz,C.
We describe our participation in the plant identification task of ImageClef 2012. We submitted two runs, one fully automatic and another one where human assistance was provided for the images in the photo category. We have not used the meta-data in either one of the systems, for exploring the extent of image analysis for the plant identification problem. Our approach in both runs employs a variety of shape, texture and color descriptors (117 in total). We have found shape to be very discriminative for isolated leaves (scan and pseudoscan categories), followed by texture. While we have experimented with color, we could not make use of the color information. We have employed the watershed algorithm for segmentation, in slightly different forms for automatic and human assisted systems. Our systems have obtained the best overall results in both automatic and manual categories, with 43% and 45% identification accuracies respectively. We have also obtained the best results on the scanned image category with 58% accuracy.
Citation Count: 1
Sabanci-Okan system at LifeCLEF 2014 Plant Identification Competition
(CEUR-WS, 2014) Yanikoglu,B.; Yildiran,S.T.; Tirkaz,C.; Aptoula,E.
We describe our system in 2014 LifeCLEF [1] Plant Identification Competition. The sub-system for isolated leaf category (LeafS-cans) was basically the same as last year [2], while plant photographs in all the remaining categories were classified using either local descriptors or deep learning techniques. However, due to large amount of data, large number of classes and shortage of time, our system was not very successful in the plant photograph sub-categories; but we obtained better results in isolated leaf images. As announced by the organizers, we obtained an inverse rank score of 0.127 overall and 0.449 for isolated leaves.