期刊
INFORMATION SCIENCES
卷 289, 期 -, 页码 148-161出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.06.025
关键词
Fuzzy shape annotation; Fuzzy shape clustering; Image annotation; Semi-supervised clustering; Shape representation
Image annotation is an important and challenging task when managing large image collections. In this paper, a fuzzy shape annotation approach for semi-automatic image annotation is presented. A fuzzy clustering process guided by partial supervision is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes representative of a number of semantic categories. Next, prototypes are manually annotated by attaching textual labels related to semantic categories. Based on the labeled proto-types, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic categories. The proposed annotation approach provides an innovative indexing method for shape-based image retrieval. Indeed, shape prototypes represent an inter-mediate indexing level that allows a faster retrieval process since a query is matched against prototypes, instead of the whole shape database, resulting in a speed up of the retrieval. The proposed approach is tested on synthetic and real-word images in order to show its suitability. (C) 2014 Published by Elsevier Inc.
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