Browsing by Author "Uhlmann, Stefan"

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Citation - WoS: 15
Citation - Scopus: 19
Collective Network of Binary Classifier Framework for Polarimetric Sar Image Classification: an Evolutionary Approach
(IEEE-Inst Electrical Electronics Engineers Inc, 2012) Kiranyaz, Serkan; İnce, Türker; Uhlmann, Stefan; Gabbouj, Moncef
Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a Divide and Conquer type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
Citation - WoS: 1
Citation - Scopus: 1
Incremental Evolution of Collective Network of Binary Classifier for Polarimetric Sar Image Classification
(IEEE, 2011) Uhlmann, Stefan; Kiranyaz, Serkan; Gabbouj, Moncef; İnce, Türker
In this paper, we propose a dedicated application of collective network of binary classifiers (CNBC) to address the problem of incremental learning, which occurs by introducing new SAR terrain classes. Furthermore, another major goal is to achieve a high classification performance over multiple SAR images even though the training data may not be entirely accurate. The CNBC in principle adopts a Divide and Conquer type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR terrain class among others and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic SAR class and feature scalability in such a way that the CNBC can gradually adapt its internal topology to new features and classes with minimal effort. Experiments visually demonstrate the classification accuracy and efficiency of the proposed system over eight fully polarimetric NASA/JPL AIRSAR data sets.
Citation - WoS: 4
Citation - Scopus: 8
Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization
(Springer, 2009) Kiranyaz, Serkan; Uhlmann, Stefan; İnce, Türker; Gabbouj, Moncef
Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique. Copyright (c) 2009 Serkan Kiranyaz et al.