Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space
Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, textu...
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| Published in: | IEEE transactions on image processing Vol. 22; no. 7; pp. 2723 - 2736 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.07.2013
Institute of Electrical and Electronics Engineers |
| Subjects: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
| Online Access: | Get full text |
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| Abstract | Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods. |
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| AbstractList | Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods. Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their ROIs (regions of interests) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain’s comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian Process Regression (GPR) algorithm, and a manifold structure is then inferred in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset have demonstrated the superiority of the proposed work in comparison with traditional methods. Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods. |
| Author | Cui, Guangbin Jiang, Xi Han, Junwei Li, Kaiming Hu, Xintao Zhu, Dajiang Guo, Lei Ji, Xiang Liu, Tianming |
| AuthorAffiliation | 3 Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China 2 Department of Computer Science and Bioimaging Research Center, The University of Georgia, USA 1 School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China |
| AuthorAffiliation_xml | – name: 1 School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China – name: 2 Department of Computer Science and Bioimaging Research Center, The University of Georgia, USA – name: 3 Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China |
| Author_xml | – sequence: 1 givenname: Junwei surname: Han fullname: Han, Junwei email: junweihan2010@gmail.com organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 2 givenname: Xiang surname: Ji fullname: Ji, Xiang email: xiangji123@gmail.com organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 3 givenname: Xintao surname: Hu fullname: Hu, Xintao email: xintao.hu@gmail.com organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Dajiang surname: Zhu fullname: Zhu, Dajiang email: dajiang.zhu@gmail.com organization: Department of Computer Science, The University of Georgia, Athens, GA, USA – sequence: 5 givenname: Kaiming surname: Li fullname: Li, Kaiming email: likaiming@gmail.com organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 6 givenname: Xi surname: Jiang fullname: Jiang, Xi email: superjx2318@gmail.com organization: Department of Computer Science, The University of Georgia, Athens, GA, USA – sequence: 7 givenname: Guangbin surname: Cui fullname: Cui, Guangbin email: cgbtd@yahoo.com.cn organization: Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China – sequence: 8 givenname: Lei surname: Guo fullname: Guo, Lei email: lguo@nwpu.edu.cn organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 9 givenname: Tianming surname: Liu fullname: Liu, Tianming email: tianming.liu@gmail.com organization: Department of Computer Science, The University of Georgia, Athens, GA, USA |
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| Keywords | Similarity Image processing Wireless telecommunication General packet radio service Mapping Texture Video signal processing Accuracy Semantics Image sequence Imaging Database Connectedness Mobile radiocommunication Computer vision Redundancy Algorithm Nuclear magnetic resonance imaging video shot retrieval Interest region Gaussian process Frame based representation Brain imaging space Signal processing Gaussian process regression Feature extraction Medical imagery functional magnetic resonance imaging Functional imaging |
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| PublicationTitle | IEEE transactions on image processing |
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| PublicationYear | 2013 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers |
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| SubjectTerms | Algorithms Applied sciences Artificial intelligence Biological and medical sciences Brain Brain imaging space Brain Mapping - methods Computer science; control theory; systems Computerized, statistical medical data processing and models in biomedicine Exact sciences and technology Feature extraction Functional magnetic resonance imaging Gaussian process regression Humans Image processing Image Processing, Computer-Assisted - methods Information theory Information, signal and communications theory Magnetic Resonance Imaging Medical management aid. Diagnosis aid Medical sciences Pattern recognition. Digital image processing. Computational geometry Regression Analysis Semantics Signal processing Streaming media Telecommunications and information theory Video Recording - methods video shot retrieval Visualization Young Adult |
| Title | Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space |
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