The Experts below are selected from a list of 37251 Experts worldwide ranked by ideXlab platform
Bernt Schiele - One of the best experts on this subject based on the ideXlab platform.
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Semantic Modeling of natural scenes for content based image retrieval
International Journal of Computer Vision, 2007Co-Authors: Julia Vogel, Bernt SchieleAbstract:In this paper, we present a novel image representation that renders it possible to access natural scenes by local Semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the Semantic content of images. The basic idea of the Semantic Modeling is to classify local image regions into Semantic concept classes such as water, rocks, or foliage. Images are represented through the frequency of occurrence of these local concepts. Through extensive experiments, we demonstrate that the image representation is well suited for Modeling the Semantic content of heterogenous scene categories, and thus for categorization and retrieval. The image representation also allows us to rank natural scenes according to their Semantic similarity relative to certain scene categories. Based on human ranking data, we learn a perceptually plausible distance measure that leads to a high correlation between the human and the automatically obtained typicality ranking. This result is especially valuable for content-based image retrieval where the goal is to present retrieval results in descending Semantic similarity from the query.
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natural scene retrieval based on a Semantic Modeling step
Conference on Image and Video Retrieval, 2004Co-Authors: Julia Vogel, Bernt SchieleAbstract:In this paper, we present an approach for the retrieval of natural scenes based on a Semantic Modeling step. Semantic Modeling stands for the classification of local image regions into Semantic classes such as grass, rocks or foliage and the subsequent summary of this information in so-called concept-occurrence vectors. Using this Semantic representation, images from the scene categories coasts, rivers/lakes, forests, plains, mountains and sky/clouds are retrieved. We compare two implementations of the method quantitatively on a visually diverse database of natural scenes. In addition, the Semantic Modeling approach is compared to retrieval based on low-level features computed directly on the image. The experiments show that Semantic Modeling leads in fact to better retrieval performance.
Julia Vogel - One of the best experts on this subject based on the ideXlab platform.
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Semantic Modeling of natural scenes for content based image retrieval
International Journal of Computer Vision, 2007Co-Authors: Julia Vogel, Bernt SchieleAbstract:In this paper, we present a novel image representation that renders it possible to access natural scenes by local Semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the Semantic content of images. The basic idea of the Semantic Modeling is to classify local image regions into Semantic concept classes such as water, rocks, or foliage. Images are represented through the frequency of occurrence of these local concepts. Through extensive experiments, we demonstrate that the image representation is well suited for Modeling the Semantic content of heterogenous scene categories, and thus for categorization and retrieval. The image representation also allows us to rank natural scenes according to their Semantic similarity relative to certain scene categories. Based on human ranking data, we learn a perceptually plausible distance measure that leads to a high correlation between the human and the automatically obtained typicality ranking. This result is especially valuable for content-based image retrieval where the goal is to present retrieval results in descending Semantic similarity from the query.
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natural scene retrieval based on a Semantic Modeling step
Conference on Image and Video Retrieval, 2004Co-Authors: Julia Vogel, Bernt SchieleAbstract:In this paper, we present an approach for the retrieval of natural scenes based on a Semantic Modeling step. Semantic Modeling stands for the classification of local image regions into Semantic classes such as grass, rocks or foliage and the subsequent summary of this information in so-called concept-occurrence vectors. Using this Semantic representation, images from the scene categories coasts, rivers/lakes, forests, plains, mountains and sky/clouds are retrieved. We compare two implementations of the method quantitatively on a visually diverse database of natural scenes. In addition, the Semantic Modeling approach is compared to retrieval based on low-level features computed directly on the image. The experiments show that Semantic Modeling leads in fact to better retrieval performance.
Zongxiang Liu - One of the best experts on this subject based on the ideXlab platform.
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a novel recursive t s fuzzy Semantic Modeling approach for discrete state space systems
Neurocomputing, 2019Co-Authors: Xiaoli Wang, Weixin Xie, Zongxiang LiuAbstract:Abstract In this paper, we propose a novel recursive Takagi-Sugeno (T-S) fuzzy Semantic Modeling approach for discrete state-space system. According to the information learning theoretic (ILT), the correntropy can capture the higher moments of the error probability distribution to deal with non-Gaussian noise. Considering the advantages of fuzzy theory and correntropy, fuzzy correntropy is constructed and a novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed to solve the premise parameter identification problem of the T-S fuzzy model. To the identification of the consequent part parameters of the T-S fuzzy model, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is presented. Moreover, to evaluate the performance of the proposed fuzzy model, the proposed T-S fuzzy model is applied to solve the problem of maneuvering target tracking by incorporating the target feature Semantic information. Finally, the experiment results show the proposed algorithm can effectively track a maneuvering target, and its performance is better than the exist algorithms, such as interacting multiple model Kalman filter (IMMKF), interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter the (IMMRBPF).
John R Smith - One of the best experts on this subject based on the ideXlab platform.
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riding the multimedia big data wave
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013Co-Authors: John R SmithAbstract:In this talk we present a perspective across multiple industry problems, including safety and security, medical, Web, social and mobile media, and motivate the need for large-scale analysis and retrieval of multimedia data. We describe a multi-layer architecture that incorporates capabilities for audio-visual feature extraction, machine learning and Semantic Modeling and provides a powerful framework for learning and classifying contents of multimedia data. We discuss the role Semantic ontologies for representing audio-visual concepts and relationships, which are essential for training Semantic classifiers. We discuss the importance of using faceted classification schemes in particular for organizing multimedia Semantic concepts in order to achieve effective learning and retrieval. We also show how training and scoring of multimedia Semantics can be implemented on big data distributed computing platforms to address both massive-scale analysis and low-latency processing. We describe multiple efforts at IBM on image and video analysis and retrieval, including IBM Multimedia Analysis and Retrieval System (IMARS), and show recent results for Semantic-based classification and retrieval. We conclude with future directions for improving analysis of multimedia through interactive and curriculum-based techniques for multimedia Semantics-based learning and retrieval.
P. Bruce Berra - One of the best experts on this subject based on the ideXlab platform.
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Semantic Modeling and knowledge representation in multimedia databases
IEEE Transactions on Knowledge and Data Engineering, 1999Co-Authors: Wasfi Al-khatib, Y. Francis Day, Abdul Ghafoor, P. Bruce BerraAbstract:In this paper, we present the current state of the art in Semantic data Modeling of multimedia data. Semantic conceptualization can be performed at several levels of information granularity, leading to multilevel indexing and searching mechanisms. Various models at different levels of granularity are compared. At the finest level of granularity, multimedia data can be indexed based on image contents, such as identification of objects and faces. At a coarser level of granularity, indexing of multimedia data can be focused on events and episodes, which are higher level abstractions. In light of the above, we also examine Modeling and indexing techniques of multimedia documents
