Human Perception & Intelligence in Multimedia Computing

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Human Perception and Intelligence in Multimedia Computing – ******Human intelligence has been driving the advancement of technology. Current state-of-the-art in the research community focuses on simulating human intelligence in a machine executable form. Machine learning methods show promise but also bring challenges due to massive training data and lengthy training time. To address these issues, it is necessary to optimize the training features using robust computer vision and signal processing techniques before the machine learning step. Otherwise, noisy and irrelevant input will lead to exponential computational time or even non-convergence in the machine training process. My research goal is to remove redundant data, identify dominant features, and introduce unsupervised algorithms to deliver intelligent multimedia computing results. ******Selecting dominant features in multimedia, e.g., image, video, signal and graphics, requires data and pattern analysis (from 1D to higher dimensions) in the spatial and temporal domain. It has been shown in the literature that a computational model incorporating human perceptual criteria, e.g., color and contrast sensitivity, is effective in feature discovery, data discrimination and target recognition. To conduct research using the rapidly growing dynamic and complex multimedia data, I will extend my perceptually motivated models and focus on introducing efficient methods in Multimedia Quality Assessment, Noise Discrimination and Multi-Modal Fusion. Dimensionality Reduction techniques will also be developed to optimize performance. ******Determination of Just-Noticeable-Difference (JND) is an important step when assessing output quality and discriminating noise. Application-specific JND threshold (or tolerance), e.g., for game applications or medical image analysis, needs to be defined in order to decide whether an output quality should be accepted or rejected. Multimedia data is represented as digital values in machines, while visual cue is interpreted based on the Human Visual System (HVS). A change in digital value may not be visible by the HVS, but such change can be an application alert. On the other hand, a visible change to the HVS is not necessarily significant for an application. The result of my study will bring further insight into the correspondence between quantitative computer output and qualitative visual assessment, and report impact which is significant to an application. ******My work on quality assessment, noise filtering and multi-modal fusion will help track changing patterns in dynamic multimedia data. Applications will include smart city monitoring, e.g., surveillance and target tracking, and the prediction of structural or geographical changes before disasters occur, e.g., landslides.