Journal ArticleDOI
Texture Classification from Random Features
Li Liu,Paul Fieguth +1 more
TLDR
The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, the approach outperforms traditional feature extraction methods which involve careful design and complex steps and leads to significant improvements in classification accuracy and reductions in feature dimensionality.Abstract:
Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag--of-words model to perform texture classification; thus, learning and classification are carried out in a compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on each of the CUReT, the Brodatz, and the MSRC databases, comparing the proposed approach to four state-of-the-art texture classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality.read more
Citations
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Real-time compressive tracking
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Fast Compressive Tracking
TL;DR: A simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with dataindependent basis that performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.
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Denoising Prior Driven Deep Neural Network for Image Restoration
TL;DR: Zhang et al. as mentioned in this paper proposed a convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images and leverages the prior of the observation model.
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Local binary features for texture classification
TL;DR: A large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets.
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Median Robust Extended Local Binary Pattern for Texture Classification
TL;DR: A comprehensive evaluation on benchmark data sets reveals MRELBP’s high performance—robust to gray scale variations, rotation changes and noise—but at a low computational cost.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.