Alfian Abdul Halin

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This paper presents a framework for soccer event detection through collaborative analysis of the textual, visual and aural modalities. The basic notion is to decompose a match video into smaller segments until ultimately the desired eventful segment is identified. Simple features are considered namely the minute-by-minute reports from sports websites (i.e.,(More)
In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands(More)
Image abstraction is an increasingly important task in various multimedia applications. It involves the artificial transformation of photorealistic images into cartoon-like images. To simplify image content , the bilateral and Kuwahara filters remain popular choices to date. However, these methods often produce undesirable over-blurring effects and are(More)
The use of physiological signals is relatively recent development in human emotion recognition. Interest in this field has been motivated by the unbiased nature of such signals, which are generated autonomously from the central nervous system. Generally, these signals can be collected from the cardiovascular system, respiratory system, electrodermal(More)
Semantic image segmentation is a fundamental yet challenging problem, which can be viewed as an extension of the conventional object detection with close relation to image segmentation and classification. It aims to partition images into non-overlapping regions that are assigned predefined semantic labels. Most of the existing approaches utilize and(More)
Automatic image annotation enables efficient indexing and retrieval of the images in the large-scale image collections, where manual image labeling is an expensive and labor intensive task. This paper proposes a novel approach to automatically annotate images by coherent semantic concepts learned from image contents. It exploits sub-visual distributions(More)