Browsing by Author "Yildiran,S.T."

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Citation Count: 0
Plant identification using local invariants;
(IEEE Computer Society, 2014) Yildiran,S.T.; Yanikoglu,B.; Abdullah,E.
We present a plant image recognition system geared towards plants with flowers. The system uses local invariants with Dense SIFT features and Bag of Visual Words representation, while the classification is done using Support Vector Machines. Our approach contains a pre-classification stage where images are categorized into color subgroups, to reduce the complexity of the problem. Using a 161-class subset of the ImageClef'2013 flower dataset, the classification accuracy is measured as %42.68, compared to %18 eithout the pre-classification. © 2014 IEEE.
Citation Count: 0
Sabanci-okan system at ImageClef 2013 plant identification competition
(CEUR-WS, 2013) Yanikoglu,B.; Aptoula,E.; Yildiran,S.T.
We describe our participation in the plant identification task of ImageClef 2013. We submitted one fully automatic run that uses different features for the uniform background (isolated leaves) and natural background (unconstrained photos) categories. Besides the category information, meta-data was only used in the natural background category. Our approach employs a variety of shape, texture and color descriptors. As in the previous years, we used shape and texture only for isolated leaves and observed them to be very effective. Our system obtained the best results in this category with a score of 0.607 which is the inverse rank of the retrieved class, averaged over all queried photos and users. As for the natural background category, we used a limited approach using a restricted set of features that were extracted globally due to lack of time, and obtained a score of 0.181.
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.