Abstract
A key issue of content-based image retrieval is exploring how to bridge the gap between the high-level semantics of an image and its lower-level properties, such as color, texture and edge. In this paper, we present a new method using perceptual edge features, called generic edge tokens (GET), as image shape content descriptors for CBIR. GETs represent basic types of perceptually distinguishable edge segments including both linear and nonlinear features, which are modeled as qualitative shape descriptors based on perceptual organization principles. In the method, an image is first transformed into GET map on the fly. The base GETs can be grouped into higher-level perceptual shape structures (PSS) as additional shape descriptors. Image content is represented statistically by perceptual feature histograms (PFHs) of GETs and PSSs. Similarity is evaluated by comparing the differences between the corresponding PFHs from two images. Experimental results are provided to demonstrate the potential of the proposed method.
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Wu, M., Gao, Q. (2005). Content-Based Image Retrieval Using Perceptual Shape Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_70
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DOI: https://doi.org/10.1007/11559573_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
Online ISBN: 978-3-540-31938-2
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