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31st Chinese Control Conference

作者： Wan Cuilan Pu Yuanyuan Liu Yuqing Xu Dan Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China

ISBN: (纸本)9789881563811

The sparse coding algorithm (SC algorithm) can remove the redundancy and obtain independent features of images. What's more, the SC algorithm can also distinguish different features. The features extracted by the SC algorithm are helpful to the analysis and classification of the visual paintings' style. In this paper, the SC algorithm are used to obtain basis functions and sparse coefficients, which are adapted and the sparsest represent of the given paintings Basis functions of the same style are the same and the response of basis functions to paintings of the same style is the sparsest. The kurtosis, which will be high when basis functions and paintings belong to the same style, was used to measure the sparseness and classify different style. The result shows that the SC algorithm can extract the essential characteristics of the images efficiently, and the classification and analysis of the style of visual paintings can be achieved.

关键词： Visual Art Style sparse coding algorithm Base Functions sparse Coefficients Kurtosis Classification

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Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images

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IET COMPUTER VISION 2015年第5期9卷 629-638页

作者： Rahmani, Masoumeh Akbarizadeh, Gholamreza Shahid Chamran Univ Ahvaz SCU Dept Elect Engn Fac Engn Ahvaz *** Iran

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 matrix factorisation (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.

关键词： synthetic aperture radar radar imaging image coding image segmentation feature extraction learning (artificial intelligence) matrix decomposition radar computing unsupervised feature learning sparse coding synthetic aperture radar images segmentation SAR image feature representation image segmentation method spectral clustering method nonnegative matrix factorisation algorithm spatial structure information feature extraction sparse coding algorithm SAR image segmentation operation spectral clustering Gaussian similarity function SAR image segmentation

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Transfer learning model for false positive reduction in lymph node detection via sparse coding and deep learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期43卷 2121-2133页

作者： Ma, Yingran Peng, Yanjun Wu, Tsu-Yang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Shandong Univ Sci & Technol Shandong Prov Key Lab Wisdom Min Informat Technol Qingdao Peoples R China

Transfer learning technique is popularly employed for a lot of medical image classification tasks. Here based on convolutional neural network (CNN) and sparse coding process, we present a new deep transfer learning architecture for false positive reduction in lymph node detection task. We first convert the linear combination of the deep transferred features to the pre-trained filter banks. Next, a new point-wise filter based CNN branch is introduced to automatically integrate transfer features for the false and positive image classification purpose. To lower the scale of the proposed architecture, we bring sparse coding process to the fixed transferred convolution filter banks. On this basis, a two-stage training strategy with grouped sparse connection is presented to train the model efficiently. The model validity is tested on lymph node dataset for false positive reduction and our approach indicates encouraging performances compared to prior approaches. Our method reaches sensitivities of 71%/85% at 3 FP/vol. and 82%/91% at 6 FP/vol. in abdomen and mediastinum respectively, which compare competitively to previous approaches.

关键词： Deep learning lymph node false positive reduction transfer learning method sparse coding algorithm

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Data Modeling Using Channel-Remapped Generalized Features

Data Modeling Using Channel-Remapped Generalized Features

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Rahmanian, Houtan Huber, Manfred Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781479906529

sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an overcomplete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to build general features that can be applicable to different sets of channels. This succinct representational model will express the stimuli independent of the locality in which they appeared. As a result, it prepares the groundwork for transferring the learned features from a set of input channels to other possible sets of input channels.

关键词： sparse coding algorithm Generalized Features Channel-Remapping

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Data Modeling Using Channel-Remapped Generalized Features

Data Modeling Using Channel-Remapped Generalized Features

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Houtan Rahmanian Manfred Huber Dept. of Computer Science and Engineering University of Texas at Arlington Arlington TX USA

ISBN: (纸本)9781479906505

关键词： sparse coding algorithm Generalized Features Channel-Remapping Data modelling sparse coding Stimuli High-level features Channels channels data entry ENTERING SETTING learning algorithms

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