Realising the spatio-temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern-discovering model is presented to identify...
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Realising the spatio-temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern-discovering model is presented to identify the traffic regularity and characteristics from spatial and temporal dimensions. To unveil this new method, there are two main parts as follows: first, by employing the constrained projected gradient of the non-negative matrixfactorisationalgorithm, the original traffic data matrix is decomposed into the feature matrix and the weight matrix. Necessary constraints are newly added so that the resulting matrices are ensured to make practical sense for reflecting the traffic spatio-temporal regular pattern. Then, the self-organising maps network is further used to cluster the factorisation error into several classes representing the disparate traffic pattern of each time. In addition, the experiment is conducted on real historical data to verify the performance of the algorithm. The global urban traffic flow for a week is summarised through a set of basic patterns with related weight distribution. The well-visualised result demonstrates that the authors method can achieve significant improvement in terms of computational efficiency and accuracy when compared with other widely-used methods.
Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral cl...
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Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL). In this method, the SAR image is primarily processed by the non-negative matrixfactorisation (NMF) algorithm and then non-negative features containing spatial structure information are extracted. Afterwards, the extracted features are learned using a sparse coding algorithm to increase the discrimination power of the features. Sparse coding is an unsupervised learning algorithm which finds the patterns or high-level semantics of the data. Ultimately, the SAR image segmentation operation is performed by applying spectral clustering on learned features. In this method, sparse coding learns features and simultaneously creates the similarity function required in spectral clustering through the production of sparse coefficients. Therefore this method avoids the Gaussian similarity function, which has a problem with scale parameter adjustment that is one of the drawbacks of spectral clustering methods. The results demonstrate that, compared with wavelet and GLCM features, NMF features manage to obtain more meaningful information and provide a better SAR image segmentation result. The results have also demonstrated that SAR image segmentation using learned features is significantly improved compared with segmentation by unlearned features. The experimental results indicate the effect of UFL on SAR image segmentation.
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