Please use this identifier to cite or link to this item: https://doi.org/10.1145/2072298.2071976
Title: Semantic point detector
Authors: Yang, K.
Zhang, L.
Wang, M. 
Zhang, H.-J.
Keywords: Semantic point detector
Issue Date: 2011
Source: Yang, K.,Zhang, L.,Wang, M.,Zhang, H.-J. (2011). Semantic point detector. MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops : 1209-1212. ScholarBank@NUS Repository. https://doi.org/10.1145/2072298.2071976
Abstract: Local features are the building blocks of many visual systems, and local point detector is usually the first component for local feature extraction. Existing local point detector are designed with target for matching and it may not perform well when applied in image content representation. Actually many existing studies demonstrate that the simple dense sampling strategy can achieve better performance than many local point detection methods in image classification tasks. In this paper, we propose a novel point detector named semantic point detector, which detects a set of semantically meaningful patches from each image and yields more compact and complete image representation. It is learned from an set of images with concepts from a large ontology. We conduct extensive experiments based on the proposed detector, and the experimental results demonstrate the effectiveness of our approach. Copyright 2011 ACM.
Source Title: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/41361
ISBN: 9781450306164
DOI: 10.1145/2072298.2071976
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