Abstract
An effective Content-Based Image Retrieval (CBIR) approach is proposed in this paper. In contrast with existing systems, the retrieval process is divided into two stages. Images are firstly classified into categories based on their semi-global features automatically using the Fuzzy C-Means (FCM) clustering algorithm, and the K Nearest Neighbor (KNN) algorithm is used to assign the query image into a proper category to get a candidate image set. As a consequence, most irrelevant images are pruned. For the second stage, a novel segmentation algorithm is applied to segment both the query image and the candidate images into regions approximately according to objects. Color and texture features are extracted from each region for finer level retrieval. The region-based features utilize local properties of objects in image, and it is suitable for complicated scenes. Finally, distance measure is applied to evaluate the image-level similarity. This coarse-to-fine mechanism provides an effective and efficient performance for our system, which is demonstrated in the experiments on the image database from COREL.
This work is supported by science foundation for young teachers of Northeast Normal University, No. 20061002, China.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lu, Y., Zhao, Q., Kong, J., Tang, C., Li, Y. (2006). A Two-Stage Region-Based Image Retrieval Approach Using Combined Color and Texture Features. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_113
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DOI: https://doi.org/10.1007/11941439_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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