Papers by Serkan Kiranyaz
Most of the content-based image retrieval (CBIR) systems frequently utilize color as a discrimina... more Most of the content-based image retrieval (CBIR) systems frequently utilize color as a discriminative feature among images, due to its robustness to noise, image degradations, and changes in resolution and orientation. While there are vast amount of color descriptors for describing global properties of colors such as " what " and " how much " color present in an image, less research has succeeded in describing the spatial properties among colors such as " where " and " how ". Color Correlogram is one of the most promising spatial color descriptors at the current state of art. However, it has several limitations, which make it infeasible even for ordinary image databases. In this paper we present a perceptual approach that eliminates such restrictions from Correlogram and further increases its discrimination power. Experimental results demonstrate Correlogram's handicaps and the success of the proposed approach in terms of retrieval accuracy and feasibility.
Abstract This paper addresses dynamic data clustering as an optimization problem and propose tech... more Abstract This paper addresses dynamic data clustering as an optimization problem and propose techniques for finding optimal (number of) clusters in a multi-dimensional data or feature space. In order to accomplish this objective we first propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex, multi-modal optimization problems at high dimensions.
Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia... more Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia indexing and retrieval scheme in order to achieve the dynamic integration and run-time execution for the following operations within the context of multimedia indexing and retrieval: Visual and aural feature extraction, shot boundary detection and spatial segmentation.
IEEE International Conference on Image Processing 2005, 2005
2007 IEEE International Conference on Signal Processing and Communications, 2007
IEEE Transactions on Multimedia, 2000
ABSTRACT Diss. -- Tampereen teknillinen yliopisto.
IEEE Transactions on Multimedia, 2007
Digital multimedia technologies, which provide powerful means to acquire and incorporate knowledg... more Digital multimedia technologies, which provide powerful means to acquire and incorporate knowledge from various sources for a broad range of applications, have a strong impact on the daily life, and have changed our way of learning, thinking and living. The rapid increase of multimedia technology over the last decade has brought about fundamental changes to computing, entertainment, and education and it has presented our computerized society with opportunities and challenges that in many cases are unprecedented. As the ...
Progressive, 2010
The recently proposed Progressive Query method is a dynamic retrieval technique, which is mainly ... more The recently proposed Progressive Query method is a dynamic retrieval technique, which is mainly designed to bring an effective solution especially for queries on large-scale multimedia databases and furthermore to provide periodic fractional query retrievals along with the ongoing query process. In this way it achieves a series of sub-query results that will eventually be converging to the full-scale search retrieval in a faster and with no minimum system requirements. Due to its pre-emptive design over a single process, the precision of ...
IET Intelligent Signal Processing Conference 2013 (ISP 2013), 2013
ABSTRACT The contemporary diagnosis of epileptic seizures is dominated by non-invasive EEG signal... more ABSTRACT The contemporary diagnosis of epileptic seizures is dominated by non-invasive EEG signal analysis and classification. In this paper, we propose a patient-specific seizure detection technique, which selects the optimal feature subsets and trains a dedicated classifier for each patient in order to maximize the classification performance. Our method exploits time domain, frequency domain, time-frequency domain and non-linear feature sets. Then, by using Conditional Mutual Information Maximization (CMIM) as the feature selection method the optimal feature subset is chosen over which the Support Vector Machine is trained as the classifier. In this study, both train and test sets contain 50% of seizure and non-seizure segments of the EEG signal. From the CHB-MIT Scalp benchmark EEG dataset, we used the EEG data from four subjects with overall 21 hours of recording. Support Vector Machine (SVM) with linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set: i.e., an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average of 93.78% sensitivity and a specificity of 99.05% are obtained using CMIM.
Image and Signal Processing for Remote Sensing XVII, 2011
ABSTRACT In this paper, we introduce dynamic and scalable Synthetic Aperture Radar (SAR) terrain ... more ABSTRACT In this paper, we introduce dynamic and scalable Synthetic Aperture Radar (SAR) terrain classification based on the Collective Network of Binary Classifiers (CNBC). The CNBC framework is primarily adapted to maximize the SAR classification accuracy on dynamically varying databases where variations do occur in any time in terms of (new) images, classes, features and users' relevance feedback. Whenever a "change" occurs, the CNBC dynamically and "optimally" adapts itself to the change by means of its topology and the underlying evolutionary method MD PSO. Thanks to its "Divide and Conquer" type approach, the CNBC can also support varying and large set of (PolSAR) features among which it optimally selects, weighs and fuses the most discriminative ones for a particular class. Each SAR terrain class is discriminated by a dedicated Network of Binary Classifiers (NBC), which encapsulates a set of evolutionary Binary Classifiers (BCs) discriminating the class with a distinctive feature set. Moreover, with each incremental evolution session, new classes/features can be introduced which signals the CNBC to create new corresponding NBCs and BCs within to adapt and scale dynamically to the change. This can in turn be a significant advantage when the current CNBC is used to classify multiple SAR images with similar terrain classes since no or only minimal (incremental) evolution sessions are needed to adapt it to a new classification problem while using the previously acquired knowledge. We demonstrate our proposed classification approach over several medium and highresolution NASA/JPL AIRSAR images applying various polarimetric decompositions. We evaluate and compare the computational complexity and classification accuracy against static Neural Network classifiers. As CNBC classification accuracy can compete and even surpass them, the computational complexity of CNBC is significantly lower as the CNBC body supports high parallelization making it applicable to grid/cloud computing.
First International Symposium on Control, Communications and Signal Processing, 2004., 2004
2013 21st Signal Processing and Communications Applications Conference (SIU), 2013
ABSTRACT In this wok, an automatic object extraction method is proposed exploiting the rich mathe... more ABSTRACT In this wok, an automatic object extraction method is proposed exploiting the rich mathematical structure of quantum mechanics. First, a novel segmentation method based on the solutions of Schrödinger's equation is proposed. Due to the large amount of segments extracted with the proposed method, the selection of the object segment is performed by maximizing a regularization energy function based on a formerly proposed edge detection algorithm indicating the object boundaries. The results of the proposed automatic object extraction method, exhibits such promising accuracy that pushes the frontier in this field to the borders of input-driven processing only.
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
2009 Seventh International Workshop on Content-Based Multimedia Indexing, 2009
2014 22nd International Conference on Pattern Recognition, 2014
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Papers by Serkan Kiranyaz