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A Benchmark for sparse coding: When Group Sparsity Meets Rank Minimization

IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 5094-5109页

作者： Zha, Zhiyuan Yuan, Xin Wen, Bihan Zhou, Jiantao Zhang, Jiachao Zhu, Ce Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Nokia Bell Labs Murray Hill NJ 07974 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Macau Dept Comp & Informat Sci Taipa 999078 Macao Peoples R China Nanjing Inst Technol Kangni Mech & Elect Inst Nanjing 211167 Peoples R China

sparse coding has achieved a great success in various image processing tasks. However, a benchmark to measure the sparsity of image patch/group is missing since sparse coding is essentially an NP-hard problem. This work attempts to fill the gap from the perspective of rank minimization. We firstly design an adaptive dictionary to bridge the gap between group-based sparse coding (GSC) and rank minimization. Then, we show that under the designed dictionary, GSC and the rank minimization problems are equivalent, and therefore the sparse coefficients of each patch group can be measured by estimating the singular values of each patch group. We thus earn a benchmark to measure the sparsity of each patch group because the singular values of the original image patch groups can be easily computed by the singular value decomposition (SVD). This benchmark can be used to evaluate performance of any kind of norm minimization methods in sparse coding through analyzing their corresponding rank minimization counterparts. Towards this end, we exploit four well-known rank minimization methods to study the sparsity of each patch group and the weighted Schatten $p$ -norm minimization (WSNM) is found to be the closest one to the real singular values of each patch group. Inspired by the aforementioned equivalence regime of rank minimization and GSC, WSNM can be translated into a non-convex weighted -norm minimization problem in GSC. By using the earned benchmark in sparse coding, the weighted -norm minimization is expected to obtain better performance than the three other norm minimization methods, i.e., -norm, -norm and weighted -norm. To verify the feasibility of the proposed benchmark, we compare the weighted -norm minimization against the three aforementioned norm minimization methods in sparse coding. Experimental results on image restoration applications, namely image inpainting and image compressive sensing recovery, demonstrate that the proposed scheme is feasible and outperforms

关键词： sparse coding GSC rank minimization adaptive dictionary weighted l(p)-norm minimization image restoration compressive sensing nuclear norm.

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sparse coding: A Deep Learning using Unlabeled Data for High - Level Representation

Sparse Coding: A Deep Learning using Unlabeled Data for High...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Vidya, R. Nasira, G. M. Priyankka, R. P. Jaia St Josephs Coll Arts & Sci Autonomous PG & Res Dept Comp Sci Cuddalore 1 Tamil Nadu India MS Univ Tirunelveli India Chikana Govt Arts Coll Dept Comp Applicat Tirupur India St Josephs Coll Arts & Sci Autonomous Comp Sci Scholar Cuddalore 1 Tamil Nadu India

ISBN: (纸本)9781479928767

sparse coding algorithm is an learning algorithm mainly for unsupervised feature for finding succinct, a little above high - level Representation of inputs, and it has successfully given a way for Deep learning. Our objective is to use High - Level Representation data in form of unlabeled category to help unsupervised learning task. when compared with labeled data, unlabeled data is easier to acquire because, unlike labeled data it does not follow some particular class labels. This really makes the Deep learning wider and applicable to practical problems and learning. The main problem with sparse coding is it uses Quadratic loss function and Gaussian noise mode. So, its performs is very poor when binary or integer value or other Non-Gaussian type data is applied. Thus first we propose an algorithm for solving the L1 - regularized convex optimization algorithm for the problem to allow High - Level Representation of unlabeled data. Through this we derive a optimal solution for describing an approach to Deep learning algorithm by using sparse code.

关键词： Deep Learning High - Level Representation sparse coding Unlabeled Data Neural Network

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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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Time warp invariant kSVD: sparse coding and dictionary learning for time series under time warp

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PATTERN RECOGNITION LETTERS 2018年 112卷 1-8页

作者： Yazdi, Saeed Varasteh Douzal-Chouakria, Ahlame Univ Grenoble Alpes CNRS Grenoble INP LIG Grenoble France

Learning dictionary for sparse representing time series is an important issue to extract latent temporal features, reveal salient primitives and sparsely represent complex temporal data. Time series are challenging data, they are often of different durations, may be composed of local or global salient events, that may arise with varying delays at different time stamps. This paper addresses the sparse coding and dictionary learning for such challenging time series. For that, we propose a non linear time warp invariant kSVD ((TWI-kSVD) where both input samples and dictionary atoms may have different lengths while involving varying delays. For the sparse coding problem, we propose an efficient time warp invariant orthogonal matching pursuit based on a new cosine maximisation time warp operator and induced sibling atoms. For the dictionary learning, thanks to a rotation transformation between each atom and its sibling atoms, a singular value decomposition is used to jointly approximate the coefficients and update the dictionary. The proposed method is confronted to major shift invariant, convolved and kernel dictionary learning methods on several challenging character and digit handwritten trajectories. The experiments conducted show the potential of TWI-kSVD to efficiently sparse represent time series and to extract latent discriminative primitives for time series classification. (C) 2018 Elsevier B.V. All rights reserved.

关键词： kSVD sparse coding Dictionary learning Time series Time warping

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Accurate HEp-2 cell classification based on sparse coding of Superpixels

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PATTERN RECOGNITION LETTERS 2016年 82卷 64-71页

作者： Ensafi, Shahab Lu, Shijian Kassim, Ashraf A. Tan, Chew Lim Natl Univ Singapore Elect & Comp Engn Fac Engn 4 Engn Dr 3Block E45-45 Singapore 117583 Singapore ASTAR Inst Infocomm Res 1 Fusionopolis Way21-01 ConnexisSouth Tower Singapore 138632 Singapore Natl Univ Singapore Sch Comp Dept Comp Sci Comp 1 13 Comp Dr Singapore 117417 Singapore

Autoimmune Diseases (AD) are among the top 10 leading causes of death in female children and women in all age groups up to 64 years. They are widely diagnosed by various antibody tests that typically apply the Indirect Immunofluorescence (IIF) to the Human Epithelial Type-2 (HEp-2) cells. Automated classification of Hep-2 cells has attracted much research interest in recent years, and many of these approaches employ patch-based models and the Bag of Words (BoW) scheme, but often face several typical constraints such as the need to process a huge number of overlapped image patches, tuning of various parameters and etc. We propose a superpixel based Hep-2 cell classification technique by calculating the sparse codes of image patches which are prepared in a more intelligent way. In particular, the super-pixel approach guides the determination of the right image patches while aggregating the neighboring pixels of similar patterns. In addition, we introduce "extended superpixels" which is able to capture the most discriminative gradient information across the boundary of the HEp-2 cell images. The proposed technique has been evaluated over two public datasets (ICPR2012 and ICIP2013) and experiments show superior performance in both classification accuracy and speed of model training and cell classification. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Autoimmune diseases HEp-2 cells Computer-aided diagnosis Cell classification Superpixel sparse coding

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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 3587-3607页

作者： Qayyum, Abdul Malik, Aamir Saeed Saad, Naufal M. Iqbal, Mahboob Abdullah, Mohd Faris Rasheed, Waqas Abdullah, Tuan A. B. Rashid Bin Jafaar, Mohd Yaqoob Univ Teknol PETRONAS CISIR Dept Elect & Elect Engn Bandar Seri Iskandar 32610 Perak Malaysia UNITEN Putrajaya CampusJalan IKRAM United Kajang 43000 Selangor Malaysia SESB Ground FloorJln Tuaran Kota Kinabalu 88400 Sabah Malaysia

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform;(b) Histogram of Oriented Gradients;and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

关键词： UAV Satellite sparse coding Dictionaries Classification SIFT DWT DRT DCT

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Computational evaluation of sparse coding on off-axis electron holograms: comparison between charge-coupled device and direct-detection device cameras

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MICROSCOPY 2022年第1期71卷 41-49页

作者： Anada, Satoshi Nomura, Yuki Hirayama, Tsukasa Yamamoto, Kazuo Japan Fine Ceram Ctr Nanostruct Res Lab Atsuta Ku 2-4-1 Mutsuno Nagoya Aichi 4568587 Japan Panasonic Corp Technol Div 3-1-1 Yagumo Nakamachi Moriguchi Osaka 5708501 Japan

The effectiveness of sparse coding for image inpainting and denoising of off-axis electron holograms was examined computationally based on hologram simulations according to considerations of two types of electron detectors, namely charge-coupled device (CCD) and direct-detection device (DDD) cameras. In this simulation, we used a simple-phase object with a phase step such as a semiconductor p-n junction and assumed that the holograms recorded by the CCD camera include shot noise, dark-current noise and read-out noise, while those recorded by the DDD camera include only shot noise. Simulated holograms with various electron doses were sparsely coded. Even though interference fringes cannot be recognized in the simulated CCD and DDD holograms when subjected to electron doses (per pixel) equal to 1 and 0.01, respectively, both the corresponding sparse-coded holograms exhibit meaningful interference fringes. We demonstrate that a combination of the DDD camera and sparse coding reduces the requisite dose used to obtain holograms to values less than one-thousandth compared with the CCD camera without image postprocessing. This combination is expected to generate lower-dose and/or higher-speed electron holography.

关键词： sparse coding electron holography image inpainting image denoising hologram simulation machine learning

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Learning discriminated and correlated patches for multi-view object detection using sparse coding

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PATTERN RECOGNITION 2017年 69卷 26-38页

作者： Yuan, Zehuan Lu, Tong Tan, Chew Lim Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore

Multi-view object detection is an open and challenging problem due to its inherent intra-class variability among discrete viewpoints. This paper aims to perform multi-view object detection by learning discriminated and correlated patches firstly and then making inference based on them. In the training stage, discriminative patches are discovered for each view by a Hough decision tree corresponding to leaf nodes with high distinctiveness and stable spatial distributions in the tree. Then discriminated patches across different views are linked to establish the correlations between any two neighboring views. During multi view detection, intra-view direct votes and inter-view transfer votes are integrated to obtain voted Hough images through a probabilistic approach with each view having one Hough image, and Mean-Shift estimation is finally employed to detect object instances and infer image viewpoint. The experiments performed on two benchmark multi-view 3D Object Category datasets and PASCAL VOC'06 Car dataset illustrate the effectiveness of the proposed framework. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-view object detection Hough decision tree Transfer vote sparse coding

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Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第9期57卷 2055-2067页

作者： Zhou, Wei Yi, Yugen Bao, Jining Wang, Wenle Shenyang Aerosp Univ Coll Comp Sci Shenyang Liaoning Peoples R China Jiangxi Normal Univ Sch Software Nanchang Jiangxi Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Univ Genoa DITEN Dept Genoa Italy

Glaucoma is a sight-threading disease which can lead to irreversible blindness. Currently, extracting the vertical cup-to-disc ratio (CDR) from 2D retinal fundus images is promising for automatic glaucoma diagnosis. In this paper, we present a novel sparse coding approach for glaucoma diagnosis called adaptive weighted locality-constrained sparse coding (AWLCSC). Different from the existing reconstruction-based glaucoma diagnosis approaches, the weighted matrix in AWLCSC is constructed by adaptively fusing multiple distance measurement information between the reference images and the testing image, making our approach more robust and effective to glaucoma diagnosis. In our approach, the disc image is firstly extracted and reconstructed according to the proposed AWLCSC technique. Then, with the usage of the obtained reconstruction coefficients and a series of reference disc images with known CDRs, the CDR of the testing disc image can be automated estimation for glaucoma diagnosis. The performance of the proposed AWLCSC is evaluated on two publicly available DRISHTI-GS1 and RIM-ONE r2 databases. The experimental results indicate that the proposed approach outperforms the state-of-the-art approaches.

关键词： Glaucoma Cup-to-disc ratio Multiple distance measurements sparse coding

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sparse coding Induced Transfer learning for HEp-2 Cell Classification

Sparse Coding Induced Transfer learning for HEp-2 Cell Class...

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2nd International Conference on Biomedical Engineering and Biotechnology (iCBEB)

作者： Liu, Anan Gao, Zan Hao, Tong Su, Yuting Yang, Zhaoxuan Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Technol Lab Comp Vis & Syst Tianjin 300191 Peoples R China Tianjin Normal Univ Coll Life Sci Tianjin 300387 Peoples R China

Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis. In this paper, we propose a sparse coding-based unsupervised transfer learning method for HEp-2 cell classification. First, the low level image feature is extracted for visual representation. Second, a sparse coding scheme with the Elastic Net penalized convex objective function is proposed for unsupervised feature learning. At last, a Support Vector Machine classifier is utilized for model learning and predicting. To our knowledge, this work is the first to transfer the human-crafted visual feature, sensitive to the variation of appearance and shape during cell movement, to the high level representation which directly denotes the correlation of one sample and the bases in the learnt dictionary. Therefore, the proposed method can overcome the difficulty in discriminative feature formulation for different kinds of cells with irregular and changing visual patterns. Large scale comparison experiments will be conducted to show the superiority of this method.

关键词： HEp-2 cell cell classification transfer learning sparse coding elastic net

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