In this paper, an efficient saliency analysis method based on multi-scale wavelet analysis is proposed, which can be used for car detection in intelligent transportation applications. In our method, saliency regions are considered as abnormal parts in a normal background; the wavelet theory is then used to detect these abnormal parts. Compared with other wavelet-based methods, our method need not perform inverse wavelet transformation, which is time-consuming. Besides, the use of multi-scale wavelet analysis can eliminate the drawbacks of the traditional center-surround methods, which have difficulties in detecting salient regions far away from object boundaries. Furthermore, a saliency prior process is adopted in our method, which can enhance the saliency map. Experimental results show that our method can achieve excellent results in terms of receiver operating characteristic (ROC) curve, the area under the curve (AUC) score, and visual performance, as compared to other state-of-the-art methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Saliency analysis based on multi-scale wavelet decomposition


    Contributors:


    Publication date :

    2013-10-01


    Size :

    639417 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Wavelet Intermittency Index-Based Saliency Measures for Texture Segmentation

    Bashar, M. K. / Ohnishi, N. / Agusa, K. | British Library Online Contents | 2005


    Saliency, Scale and Image Description

    Kadir, T. / Brady, M. | British Library Online Contents | 2001


    Visual Saliency Oriented Vehicle Scale Estimation

    Ding, Jiali / Liu, Tie / Chen, Qixin et al. | British Library Conference Proceedings | 2020


    Multi-Scale Noise Reduction Based Wavelet

    Li, Rui-Xian | Tema Archive | 2014


    Bayesian optimization of the scale saliency filter

    Suau, P. / Escolano, F. | British Library Online Contents | 2008