Abstract
Automatic feature extraction combined with proper similarity measurement plays an important role in Content-based Image Retrieval(CBIR). This paper introduces a new similarity measurement named Weighted Main Colors First (WMCF), derived from three conditions to approximate human perception, to improve retrieval performance in CBIR. Meanwhile, the texture feature (Ptex) for CBIR is extracted by using unit-linking Pulse Coupled Neural Network (PCNN). This PCNN-based texture feature consists of a series of image gradient entropy values. Experimental results show that Ptex distinguishes different textures very well, and that WMCF has better performance than Comparing Histogram by Clustering (CHIC) and Optimal Color Composition Distance (OCCD) with much lower time complexity. Compared with Fixed Cardinality (FC), Block Difference of Inverse Probabilities (BDIP) and Normalized Moment of Inertia (Nmi), our approach makes 7% improvement and obtains a better ANMRR (Average Normalized Modified Retrieval Rank).
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Yang, C., Gu, X. (2014). Image Retrieval Using a Novel Color Similarity Measurement and Neural Networks. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_4
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DOI: https://doi.org/10.1007/978-3-319-12643-2_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
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