Abstract
By using support vector machine (SVM), this paper presents a novel face retrieval scheme in face database based on lifting wavelets features. The relevance feedback mechanism is also performed. The scheme can be described in three stages as follows. First, lifting wavelets decomposition technique is employed because it not only can extract the optimal intrinsic features for representing a face image, but also can accelerate the speed of the wavelets transform. Second, Linear Discriminant Analysis (LDA) is adopted to reduce the feature dimensionality and enhance the class discriminability. Third, relevance feedback using SVM is applied to learn on user’s feedback to refine the retrieval performance. The experimental evaluation has been conducted on ORL dataset in which the results show that our proposed approach is effective and promising.
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Wong, C.F., Zhu, J., Vai, M.I., Mak, P.U., Ye, W. (2006). Face Retrieval with Relevance Feedback Using Lifting Wavelets Features. In: Qian, T., Vai, M.I., Xu, Y. (eds) Wavelet Analysis and Applications. Applied and Numerical Harmonic Analysis. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7778-6_35
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DOI: https://doi.org/10.1007/978-3-7643-7778-6_35
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