Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
A perceptual grouping hierarchy for appearance-based 3D object recognition
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Fundamentals in Computer Vision: An Advanced Course
Fundamentals in Computer Vision: An Advanced Course
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Grounded semantic composition for visual scenes
Journal of Artificial Intelligence Research
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Scene understanding addresses the issue of "what a scene contains". Existing research on scene understanding is typically focused on classifying a scene into classes that are of the same category type. These approaches, although they solve some scene-understanding tasks successfully, in general fail to address the semantics in scene understanding. For example, how does an agent learn the concept label "red" and "ball" without being told that it is a color or a shape label in advance? To cope with this problem, we have proposed a novel research called semantic scene concept learning. Our proposed approach models the task of scene understanding as a "multi-labeling" classification problem. Each scene instance perceived by the agent may receive multiple labels coming from different concept categories, where the goal of learning is to let the agent discover the semantic meanings, i.e., the set of relevant visual features, of the scene labels received. Our preliminary experiments have shown the effectiveness of our proposed approach in solving this special intra- and inter-category mixing learning task.