What makes a good movie trailer? It's a big challenge to answer this question because of the complexity of multimedia in both low level sensory features and high level semantic features. However, human perception and reactivity could be straightforward evidence for evaluation. Modern Electro-encephalography (EEG) technology provides measurement of consequential brain neural activity to external stimuli. Meanwhile, visual perception and attention could be captured and interpreted by Eye Tracking technology. Intuitively, simultaneous EEG and Eye Tracker recording of human audience with multimedia stimuli could bridge the gap between human comprehension and multimedia analysis, and provide a new way for movie trailer evaluation. In this paper, we propose a novel platform to simultaneously record EEG and eye movement for participants with video stimuli by integrating 256-channel EEG, Eye Tracker and video display device as a system. Based on the proposed system a novel experiment has been designed, in which independent and joint features of EEG and Eye tracking data were mined to evaluate the movie trailer. Our analysis has shown interesting features that are corresponding with trailer quality and video shoot changes.

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.

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks-DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

The multimedia content analysis community has made significant efforts to bridge the gaps between low-level features and high-level semantics perceived by humans. Recent advances in brain imaging and neuroscience in exploring the human brain's responses during multimedia comprehension demonstrated the possibility of leveraging cognitive neuroscience knowledge to bridge the semantic gaps. This paper presents our initial effort in this direction by using functional magnetic resonance imaging (fMRI). Specifically, task-based fMRI (T-fMRI) was performed to accurately localize the brain regions involved in video comprehension. Then, natural stimulus fMRI (N-fMRI) data were acquired when subjects watched the multimedia clips selected from the TRECVID datasets. The responses in the localized brain regions were measured and used to extract high-level features as the representation of the brain's comprehension of semantics in the videos. A novel computational framework was developed to learn the most relevant low-level feature sets that best correlate the fMRI-derived semantic features based on the training videos with fMRI scans, and then the learned model was applied to larger scale TRECVID video datasets without fMRI scans for category classification. Our experimental results demonstrate: 1) there are meaningful couplings between brain's fMRI-derived responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI and 2) the computationally learned low-level features can significantly (p <; 0.01) improve video classification in comparison with original low-level features and extracted low-level features resulted from well-known feature projection algorithms.

In recent two decades, a large amount of effort has been devoted to content-based video retrieval (CBVR), which aims to manage large-scale video databases in an effective way based on visual features such as color, shape, texture, and motion. However, the performance of CBVR systems is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper proposes a novel retrieval methodology using semantic features derived from brain imaging space (BIS) that reflects brain responses and interactions under natural stimulus of video watching. A mapping from visual features to semantic features in BIS is built through Gaussian process regression. A manifold structure is then inferred where video key frames are represented by mapped feature vectors in BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames. Preliminary experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.

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.

Functional segregation and integration are fundamental characteristics of the human brain. Studying the connectivity among segregated regions and the dynamics of integrated brain networks has drawn increasing interest. A very controversial, yet fundamental issue in these studies is how to determine the best functional brain regions or ROIs (regions of interests) for individuals. Essentially, the computed connectivity patterns and dynamics of brain networks are very sensitive to the locations, sizes, and shapes of the ROIs. This paper presents a novel methodology to optimize the locations of an individual's ROIs in the working memory system. Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints. The optimization problem is solved via the simulated annealing approach. Our experimental results show that the optimized ROIs have significantly improved consistency in structural and functional profiles across subjects, and have more reasonable localizations and more consistent morphological and anatomic profiles.