Browsing by Author "Tai, Chiew-Lan"
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Item Context-based Sketch Classification(ACM, 2018) Zhang, Jianhui; Chen, Yilan; Li, Lei; Fu, Hongbo; Tai, Chiew-Lan; Aydın, Tunç and Sýkora, DanielWe present a novel context-based sketch classification framework using relations extracted from scene images. Most of existing methods perform sketch classification by considering individually sketched objects and often fail to identify their correct categories, due to the highly abstract nature of sketches. For a sketched scene containing multiple objects, we propose to classify a sketched object by considering its surrounding context in the scene, which provides vital cues for resolving its recognition ambiguity. We learn such context knowledge from a database of scene images by summarizing the inter-object relations therein, such as co-occurrence, relative positions and sizes.We show that the context information can be used for both incremental sketch classification and sketch co-classification. Our method outperforms the state-of-the-art single-object classification method, evaluated on a new dataset of sketched scenes.Item TAVE: Template-based Augmentation of Visual Effects to Human Actions in Videos(The Eurographics Association, 2018) Liu, Jingyuan; Zhou, Xuren; Fu, Hongbo; Tai, Chiew-Lan; Fu, Hongbo and Ghosh, Abhijeet and Kopf, JohannesWe present TAVE, a framework that allows novice users to add interesting visual effects by mimicking human actions in a given template video, in which pre-defined visual effects have already been associated with specific human actions. Our framework is mainly based on high-level features of human pose extracted from video frames, and uses low-level image features as the auxiliary information. We encode an action into a set of code sequences representing joint motion directions and use a finite state machine to recognize the action state of interest. The visual effects, possibly with occlusion masks, can be automatically transferred from the template video to a target video containing similar human actions.