Browsing by Author "Yanikoglu,B."

Now showing 1 - 9 of 9

Citation Count: 0
Fruit detection with binary partition trees;
(Institute of Electrical and Electronics Engineers Inc., 2016) Ozdemir,M.C.; Aptoula,E.; Yanikoglu,B.
In this study, binary partition trees are applied to the problem of fruit detection. The fact that binary partition trees are inherently unbiased and independent of flatzones is the main reason for this application. Using only circularity from the shape priors, this system is put to test with 39 images of three classes of fruits and the test results show an average of 0.669 precision and 0.851 recall. © 2016 IEEE.
Citation Count: 39
Morphological features for leaf based plant recognition
(IEEE Computer Society, 2013) Aptoula,E.; Yanikoglu,B.
Although plant recognition has become an increasingly popular research topic, it remains nonetheless a scientific and technical challenge. Besides all the difficulties of classic object recognition, such as illumination, viewpoint and scale variations, plants can additionally exhibit visual changes depending on their age and condition, thus demanding a specialized approach. In this paper, we present two descriptors based on mathematical morphology; the first consists of the computation of morphological covariance on the leaf contour profile and the second is an extension of the recently introduced circular covariance histogram, capturing leaf venation characteristics. The effectiveness of both descriptors has been validated with the ImageClef'12 plant identification dataset. © 2013 IEEE.
Citation Count: 3
On the effect of synthetic morphological feature vectors on hyperspectral image classification performance
(Institute of Electrical and Electronics Engineers Inc., 2015) Davari,A.A.; Aptoula,E.; Yanikoglu,B.
This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Based on experiments conducted on multiple hyperspectral datasets, it has been observed that synthetic feature vectors contribute considerably to classification performance in the case of limited training dataset sizes. © 2015 IEEE.
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: 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: 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.
Citation Count: 2
Sabanci-Okan system in LifeCLEF 2015 plant identification competition
(CEUR-WS, 2015) Ghazi,M.M.; Yanikoglu,B.; Aptoula,E.; Muslu,O.; Ozdemir,M.C.
We present our deep learning based plant identification system in the LifeCLEF 2015. The approach is based on a simple deep convolutional network called PCANet and does not require large amounts of data due to using principal component analysis to learn the weights. After learning multistage filter banks, a simple binary hashing is applied to the filtered data, and features are pooled from block histograms. A multiclass linear support vector machine is then trained and the system is evaluated using the plant task datasets of LifeCLEF 2014 and 2015. As announced by the organizers, our submission achieved an overall inverse rank score of 0.153 in the image-based and an inverse rank score of 0.162 in the observation-based task of LifeCLEF 2015, as well as an inverse rank score of 0.51 for the LeafScan dataset of LifeCLEF 2014.