Paper Info
Title
Bridging low-level features and high-level semantics via fMRI brain imaging for video classification
Abstract
The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two fields of multimedia analysis and brain imaging, both topics of low-level features and high level semantics are extensively studied. For instance, in the multimedia analysis field, many algorithms are available for multimedia feature extraction, and benchmark datasets are available such as the TRECVID. In the brain imaging field, brain regions that are responsible for vision, auditory perception, language, and working memory are well studied via functional magnetic resonance imaging (fMRI). This paper presents our initial effort in marrying these two fields in order to bridge the gaps between low-level features and high-level semantics via fMRI brain imaging. Our experimental paradigm is that we performed fMRI brain imaging when university student subjects watched the video clips selected from the TRECVID datasets. At current stage, we focus on the three concepts of sports, weather, and commercial-/advertisement specified in the TRECVID 2005. Meanwhile, the brain regions in vision, auditory, language, and working memory networks are quantitatively localized and mapped via task-based paradigm fMRI, and the fMRI responses in these regions are used to extract features as the representation of the brain's comprehension of semantics. Our computational framework aims to learn the most relevant low-level feature sets that best correlate the fMRI-derived semantics based on the training videos with fMRI scans, and then the learned models are applied to larger scale test datasets without fMRI scans for category classifications. Our result shows that: 1) there are meaningful couplings between brain's fMRI responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI; 2) The computationally learned low-level feature sets from fMRI-derived semantic features can significantly improve the classification of video categories in comparison with that based on original low-level features.
Year
DOI
Venue
2010
10.1145/1873951.1874016
ACM Multimedia 2001
Keywords
Field
DocType
fmri scan,brain imaging,high-level semantics,video classification,fmri brain imaging,task-based paradigm fmri,brain imaging field,brain region,low-level feature,low-level feature set,fmri response,semantics,brain computer interface,feature extraction,working memory
Computer vision,Functional magnetic resonance imaging,TRECVID,Computer science,Brain–computer interface,Working memory,Feature extraction,Artificial intelligence,Natural language processing,Neuroimaging,Perception,Semantics
Conference
Citations 
PageRank 
References 
16
0.97
14
Authors
17
Name
Order
Citations
PageRank
Xintao Hu111813.53
Fan Deng2967.56
Kaiming Li338530.92
Tuo Zhang423332.92
Hanbo Chen528727.40
Xi Jiang631137.88
Jinglei Lv720526.70
Dajiang Zhu832036.72
Carlos Faraco91077.00
Degang Zhang1012810.01
Arsham Mesbah11241.89
Junwei Han123501194.57
Xian-Sheng Hua136566328.17
Li Xie14434.86
L. Stephen Miller151309.26
Lei Guo161661142.63
Tianming Liu171033112.95