Browsing by Author "Srinivasan, Kathiravan"

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Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data
(2020-01-01)
Kumar, Akshi
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Srinivasan, Kathiravan
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Cheng Wen-Huang
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Zomaya, Albert Y.
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電子工程學系及電子研究所
Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBovw model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m epsilon {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely 'highly positive,' positive,"neutral,"negative' and 'highly negative.' The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually.
VEHICLE DETECTION IN THERMAL IMAGES USING DEEP NEURAL NETWORK
(2018-01-01)
Chang, Chin-Wei
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Srinivasan, Kathiravan
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Chen, Yung-Yao
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Cheng, Wen-Huang
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Hua, Kai-Lung
In today's world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. We propose a real-time vehicle detection using a sequence of night-time thermal images. Moreover, the thermal images have the capability of retaining even the minuscule vehicle details in a dim environment. For an efficient vehicle detection, the thermal image dataset collected during the dusk and night is used for training purposes. Subsequently, the contrast enhancement and sharpening of these images are performed using the Thermal Feature Enhancement (TFE). Then the concatenated images are supplied as the input to allow the model to learn more effectively. Besides, we also propose an improved convolution network model entitled as the Thermal Image Only Looked Once (TOLO) model for vehicle detection. Additionally, we propose a method called as Low Probability Candidate Filter (LPCF) to compensate the probability of not-easy-to-detect vehicles. Our proposed method produces better results for the F1-measure in comparison with existing methods.