Browsing by Subject "Data fusion"

Now showing 1 - 9 of 9

Open Access
Automatic ranking of information retrieval systems using data fusion
(Elsevier Ltd, 2006-05) Nuray, R.; Can, F.
Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the "(pseudo) relevant documents," and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods. © 2005 Elsevier Ltd. All rights reserved.
Open Access
An experimental setup for performance analysis of an online adaptive cooperative spectrum sensing scheme for both in-phase and quadrature branches
(IEEE, 2011) Yarkan, S.; Qaraqe, K.A.; Töreyin, B.U.; Çetin, A. Enis
Spectrum sensing is one of the most essential characteristics of cognitive radios (CRs). Robustness and adaptation to varying wireless propagation scenarios without compromising the sensing accuracy are desirable features of any spectrum sensing method to be deployed in CR systems. In this study, an online adaptive cooperation technique for spectrum sensing is proposed in order to maintain the level of reliability and performance. Cooperation is achieved by sensors which employ energy detection. These sensors send their output to a center where data fusion operation is carried out in an online and adaptive manner. Adaptation is realized by the use of orthogonal projections onto convex sets (POCS). In conjunction with the proposed method, an end-to-end methodology for a flexible experimental setup is also proposed in this study. This setup is arranged to emulate the proposed adaptive cooperation scheme for spectrum sensing and validate its practical use in cognitive radio systems. Comparative performance results for both inphase and quadrature branches are presented. © 2011 IEEE.
Open Access
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
(Institute of Electrical and Electronics Engineers, 1998-11) Alatan, A. A.; Onural, L.; Wollborn, M.; Mech, R.; Tuncel, E.; Sikora, T.
Flexibility and efficiency of coding, content extraction, and content-based search are key research topics in the field of interactive multimedia. Ongoing ISO MPEG-4 and MPEG-7 activities are targeting standardization to facilitate such services. European COST Telecommunications activities provide a framework for research collaboration. COST 211 bis and COST 211 tcr activities have been instrumental in the definition and development of the ITU-T H.261 and H.263 standards for video-conferencing over ISDN and videophony over regular phone lines, respectively. The group has also contributed significantly to the ISO MPEG-4 activities. At present a significant effort of the COST 211 tcr group activities is dedicated toward image and video sequence analysis and segmentation - an important technological aspect for the success of emerging object-based MPEG-4 and MPEG-7 multimedia applications. The current work of COST 211 is centered around the test model, called the Analysis Model (AM). The essential feature of the AM is its ability to fuse information from different sources to achieve a high-quality object segmentation. The current information sources are the intermediate results from frame-based (still) color segmentation, motion vector based segmentation, and change-detection-based segmentation. Motion vectors, which form the basis for the motion vector based intermediate segmentation, are estimated from consecutive frames. A recursive shortest spanning tree (RSST) algorithm is used to obtain intermediate color and motion vector based segmentation results. A rule-based region processor fuses the intermediate results; a postprocessor further refines the final segmentation output. The results of the current AM are satisfactory; it is expected that there will be further improvements of the AM within the COST 211 project.
Open Access
Implicit concept drift detection for multi-label data streams
(2022-01) Gülcan, Ege Berkay
Many real-world applications adopt multi-label data streams as the need for algo-rithms to deal with rapidly generated data increases. For such streams, changes in data distribution, also known as concept drift, cause the existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an implicit (un-supervised) concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 12 datasets and a baseline classifier. The results show that LD3 provides between 19.8% and 68.6% better predictive performance than comparable detectors on both real-world and synthetic data streams.
Open Access
Interactive training of advanced classifiers for mining remote sensing image archives
(ACM, 2004) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio G.
Advances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.
Open Access
Object detection using optical and LiDAR data fusion
(IEEE, 2016-07) Taşar, Onur; Aksoy, Selim
Fusion of aerial optical and LiDAR data has been a popular problem in remote sensing as they carry complementary information for object detection. We describe a stratified method that involves separately thresholding the normalized digital surface model derived from LiDAR data and the normalized difference vegetation index derived from spectral bands to obtain candidate image parts that contain different object classes, and incorporates spectral and height data with spatial information in a graph cut framework to segment the rest of the image where such separation is not possible. Experiments using a benchmark data set show that the performance of the proposed method that uses small amount of supervision is compatible with the ones in the literature. © 2016 IEEE.
Open Access
Object detection using optical and lidar data fusion with graph-cuts
(2017-03) Taşar, Onur
Object detection in remotely sensed data has been a popular problem and is commonly used in a wide range of applications in domains such as agriculture, navigation, environmental management, urban monitoring and mapping. However, using only one type of data source may not be sufficient to solve this problem. Fusion of aerial optical and LiDAR data has been a promising approach in remote sensing as they carry complementary information for object detection. We propose frameworks that partition the data in multiple levels and detect objects with minimal supervision in the partitioned data. Our methodology involves thresholding the data according to height, and dividing the data into smaller components to process it efficiently in the preprocessing step. For the classification task, we propose two graph cut based procedures that detect objects in each component using height information from LiDAR, spectral information from aerial data, and spatial information from adjacency maps. The first procedure provides a binary classification, whereas the second one performs a multi-class classification. We use the first framework to separate buildings from trees in the high pixels, and roads from grass areas in the low pixels. The second procedure is used to detect all of the classes in each component at once. The only supervision our proposed methodology requires consists of samples that are used to estimate the weights of the edges in the graph for the graph-cut procedures. Experiments using a benchmark data set show that the performance of the proposed methodology that uses small amount of supervision is compatible with the ones in the literature.
Open Access
An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method
(Institute of Electrical and Electronics Engineers, 2012) Yarkan S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. Enis
Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.
Open Access
Video object segmentation for interactive multimedia
(1998) Ekmekçi, Tolga
Recently, trends in video processing research have shifted from video compression to video analysis, due to the emerging standards MPEG-4 and MPEG-7. These standards will enable the users to interact with the objects in the audiovisual scene generated at the user’s end. However, neither of them prescribes how to obtain the objects. Many methods have been proposed for segmentation of video objects. One of the approaches is the “Analysis Model” (AM) of European COST-211 project. It is a modular approach to video object segmentation problem. Although AM performs acceptably in some cases, the results in many other cases are not good enough to be considered as semantic objects. In this thesis, a new tool is integrated and some modules are replaced by improved versions. One of the tools uses a block-based motion estimation technique to analyze the motion content within a scene, computes a motion activity parameter, and skips frames accordingly. Also introduced is a powerful motion estimation method which uses maximum a posteriori probability (MAP) criterion and Gibbs energies to obtain more reliable motion vectors and to calculate temporally unpredictable areas. To handle more complex motion in the scene, the 2-D affine motion model is added to the motion segmentation module, which employs only the translational model. The observed results indicate that the AM performance is improved substantially. The objects in the scene and their boundaries are detected more accurately, compared to the previous results.