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检索条件"主题词=sparse coding"

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Learned dictionaries for sparse image representation: Properties and results

Learned dictionaries for sparse image representation: Proper...

引用

Conference on Wavelets and Sparsity XIV

作者： Skretting, Karl Engan, Kjersti Univ Stavanger Dept Elect Eng & Comp Engn Stavanger Norway

ISBN: (纸本)9780819487483

sparse representation of images using learned dictionaries have been shown to work well for applications like image denoising, impainting, image compression, etc. In this paper dictionary properties are reviewed from a theoretical approach, and experimental results for learned dictionaries are presented. The main dictionary properties are the upper and lower frame (dictionary) bounds, and (mutual) coherence properties based on the angle between dictionary atoms. Both l(0) sparsity and l(1) sparsity are considered by using a matching pursuit method, order recursive matching Pursuit (ORMP), and a basis pursuit method, i.e., LARS or Lasso. For dictionary learning the following methods are considered: Iterative least squares (ILS-DLA or MOD), recursive least squares (RLS-DLA), K-SVD and online dictionary learning (ODL). Finally, it is shown how these properties relate to an image compression example.

关键词： sparse coding dictionary learning dictionary properties image compression

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Knowledge Transfer with Low-Quality Data: a Feature Extraction Issue

Knowledge Transfer with Low-Quality Data: a Feature Extracti...

引用

IEEE 27th International Conference on Data Engineering (ICDE 2011)

作者： Quanz, Brian Huan, Jun Mishra, Meenakshi Univ Kansas Dept Elect Engn & Comp Sci Informat & Telecommun Technol Ctr Lawrence KS 66045 USA

ISBN: (纸本)9781424489589

Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards seamless knowledge transfer among tasks, effective representation of the data is a critical but yet not fully explored research area for the data engineer and data miner. Here we present a technique based on the idea of sparse coding, which essentially attempts to find an embedding for the data by assigning feature values based on subspace cluster membership. We modify the idea of sparse coding by focusing the identification of shared clusters between data when source and target data may have different distributions. In our paper, we point out cases where a direct application of sparse coding will lead to a failure of knowledge transfer. We then present the details of our extension to sparse coding, by incorporating distribution distance estimates for the embedded data, and show that the proposed algorithm can overcome the shortcomings of the sparse coding algorithm on synthetic data and achieve improved predictive performance on a real world chemical toxicity transfer learning task.

关键词： Encoding Equations Estimation Feature extraction Kernel Knowledge transfer Optimization chemical toxicity transfer learning task data cluster data engineer data mining data mining data representation data source data structures embedded data encoding feature extraction feature extraction knowledge management knowledge transfer learning (artificial intelligence) low quality data pattern clustering sparse coding subspace cluster membership synthetic data

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LEARNING INVARIANT COLOR FEATURES WITH sparse TOPOGRAPHIC RESTRICTED BOLTZMANN MACHINES

LEARNING INVARIANT COLOR FEATURES WITH SPARSE TOPOGRAPHIC RE...

引用

18th IEEE International Conference on Image Processing (ICIP)

作者： Goh, Hanlin Kusmierz, Lukasz Lim, Joo-Hwee Thome, Nicolas Cord, Matthieu ASTAR Inst Infocomm Res Singapore Singapore UPMC Sorbonne Univ Lab Informat Paris 6 Paris France

ISBN: (纸本)9781457713033

Our objective is to learn invariant color features directly from data via unsupervised learning. In this paper, we introduce a method to regularize restricted Boltzmann machines during training to obtain features that are sparse and topographically organized. Upon analysis, the features learned are Gabor-like and demonstrate a coding of orientation, spatial position, frequency and color that vary smoothly with the topography of the feature map. There is also differentiation between monochrome and color filters, with some exhibiting color-opponent properties. We also found that the learned representation is more invariant to affine image transformations and changes in illumination color.

关键词： Unsupervised feature learning invariant features sparse coding topographic coding color features

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SENSC Algorithm for Object & Scene Categorization

SENSC Algorithm for Object & Scene Categorization

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Conference on Multimedia on Mobile Devices and Multimedia Content Access - Algorithms and Systems V

作者： Liu, Bao-di Zhang, Yu-Jin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9780819484185

So far, the most popular method for object & scene categorization (such as Vector Quantization (VQ), sparse coding (SC)) transforms low-level descriptors (usually SIFT descriptors) into mid-level representations with more meaningful information. These methods have two key steps: (1) the building dictionary step, which provides a mechanism to map low-level descriptors into mid-level representations. (2) The coding step, which implements the map from low-level descriptors to mid-level representation for each image by the dictionary. In this paper, we proposed to use a stable and efficient nonnegative sparse coding (SENSC) algorithm for building dictionary and coding each image with it to develop an extension of Spatial Pyramid Matching (SPM) method. We also compare SENSC with SC (state-of-the-art performance) method and VQ method, analysis the drawbacks of SC and VQ for building dictionary, and show SENSC algorithm's performance. According to the experiments on three benchmarks (Caltech101, scene, and events), the method we proposed has shown a better performance than SC and VQ methods.

关键词： Vector Quantization sparse coding nonnegative sparse coding dictionary

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SUPER-RESOLUTION OF MUSICAL SIGNALS USING APPROXIMATE MATCHING PURSUIT

SUPER-RESOLUTION OF MUSICAL SIGNALS USING APPROXIMATE MATCHI...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Keegan, Brennan P. Tjoa, Steven K. Liu, K. J. Ray Univ Maryland Dept Elect & Comp Engn Signals & Informat Grp College Pk MD 20742 USA

ISBN: (纸本)9781457706936

Super-resolution (SR) is a well-studied problem in signal processing, particularly with regard to image and video applications. SR techniques are useful because unlike simple interpolation, they create a high-resolution signal from a low-resolution input by generating new information that was not previously present. A growing body of research shows progress in development of SR techniques using dictionary learning. We propose a method for SR of musical signals through matrix factorization and use of a known musical dictionary. The approximate matching pursuit (AMP) algorithm is used to query the dictionary and perform factorization, making the overall process efficient and scalable. By approximating the frequency information of a low-resolution input spectrogram as the linear combination of entries in the musical dictionary, we are able to closely match the input signal's missing high frequency information and thereby create a high-resolution output signal.

关键词： Nonnegative matrix factorization sparse coding signal restoration and enhancement

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A COCHLEAR NEURON BASED ROBUST FEATURE FOR SPEAKER RECOGNITION

A COCHLEAR NEURON BASED ROBUST FEATURE FOR SPEAKER RECOGNITI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： You, Datao Jiang, Tao Han, Jiqing Zheng, Tieran Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China

ISBN: (纸本)9781457705397

In this paper, a robust feature for text-independent speaker recognition is proposed, which simulate the response mode of cochlear neurons in processing acoustic signal. The feature is derived from sparse coding coefficient which is computed on a learned over-complete dictionary, and the dictionary is considered similar to part of speech sensitive cochlear neurons. Furthermore, the feature is generated without dimension reducing and de-correlation. The robust feature is implemented to address the problem of mismatch situation between training and testing. Experiments show that the proposed feature outperforms the Mel-frequency cepstral coefficients (MFCC) feature, especially under noisy environments, the equal error rate (EER) of the MFCC drops to 21.6% (10 dB) from 10.3% (25 dB), while the EER of the proposed feature is also 6.6% (10 dB) with no degradation.

关键词： Robust feature extraction cochlear neurons speaker recognition sparse coding auditory

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On the Application of Structured sparse Model Selection to JPEG Compressed Images

On the Application of Structured Sparse Model Selection to J...

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3rd International Computational Color Imaging Workshop

作者： Farinella, Giovanni Maria Battiato, Sebastiano Univ Catania Dipartimento Matemat & Informat Image Proc Lab I-95125 Catania Italy

ISBN: (纸本)9783642204036

The representation model that considers an image as a sparse linear combination of few atoms of a predefined or learned dictionary has received considerable attention in recent years. Among the others, the Structured sparse Model Selection (SSMS) was recently introduced. This model outperforms different state-of-the-art algorithms in a number of imaging tasks (e.g., denoising, deblurring, inpainting). Despite the high denoising performances achieved by SSMS have been demonstrated, the compression issues has been not considered during the evaluation. In this paper we study the performances of SSMS under lossy JPEG compression. Experiments have shown that the SSMS method is able to restore compressed noisy images with a significant margin, both in terms of PSNR and SSIM quality measure, even though the original framework is not tuned for the specific task of compression. Quantitative and qualitative results pointed out that SSMS is able to perform both denoising and compression artifacts reduction (e.g., deblocking), by demonstrating the promise of sparse coding methods in application where different computational engines are combined to generate a signal (e.g., Imaging Generation Pipeline of single sensor devices).

关键词： sparse coding Inverse Problems Compression Denoising Image Enhancement Image Restoration

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SPATIAL COORDINATE coding TO REDUCE HISTOGRAM REPRESENTATIONS, DOMINANT ANGLE AND COLOUR PYRAMID MATCH

SPATIAL COORDINATE CODING TO REDUCE HISTOGRAM REPRESENTATION...

引用

18th IEEE International Conference on Image Processing (ICIP)

作者： Koniusz, Piotr Mikolajczyk, Krystian Univ Surrey Guildford GU2 5XH Surrey England

ISBN: (纸本)9781457713033

Spatial Pyramid Match lies at a heart of modern object category recognition systems. Once image descriptors are expressed as histograms of visual words, they are further deployed across spatial pyramid with coarse-to-fine spatial location grids. However, such representation results in extreme histogram vectors of 200K or more elements increasing computational and memory requirements. This paper investigates alternative ways of introducing spatial information during formation of histograms. Specifically, we propose to apply spatial location information at a descriptor level and refer to it as Spatial Coordinate coding. Alternatively, x, y, radius, or angle is used to perform semi-coding. This is achieved by adding one of the spatial components at the descriptor level whilst applying Pyramid Match to another. Lastly, we demonstrate that Pyramid Match can be applied robustly to other measurements: Dominant Angle and Colour. We demonstrate state-of-the art results on two datasets with means of Soft Assignment and sparse coding.

关键词： Image classification spatial pyramid match dominant angle colour soft assignment sparse coding bags-of-words

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Joint Development of Disparity Tuning and Vergence Control

Joint Development of Disparity Tuning and Vergence Control

引用

作者： Wanting SUN Hong Kong University of Science and Technology

学位级别：硕士

Behavior and sensory perception are mutually dependent. Sensory perception drives behavior, but behavior also influences the development of sensory perception, by altering the statistics of the sensory input. Thus, there is a "chicken-and-egg" problem as to which arises first. We propose here a solution to this problem in the context of the neural processing of binocular disparity and the behavioral control of binocular vergence to maintain fixation. We show that it is possible for both the neural processing and the control policy to be learned simultaneously. In particular, we assume that the neural processing develops through learning a sparse complex-cell representation of the input, and that the control policy simultaneously develops through reinforcement learning to maximize the activity in this complex cell representation. These processes are coupled. The control policy determines the statistics of the input, which determines the sparse coding that develops, which in turn determines the reward maximized by the control policy. Our experiments show that both disparity selective binocular receptive fields and a successful binocular fixation policy develop. Our results underline the importance of behavior, as we show that on the same input but in the absence of learned behavior, much fewer disparity selective binocular receptive fields develop.

关键词： Binocular vision vergence control reinforcement learning sparse coding neural development

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Tag Localization with Spatial Correlations and Joint Group Sparsity

Tag Localization with Spatial Correlations and Joint Group S...

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IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Yang Yang, Yi Huang, Zi Shen, Heng Tao Nie, Feiping Univ Queensland Brisbane Qld 4072 Australia Univ Texas Arlington Arlington TX USA

ISBN: (纸本)9781457703935

Nowadays numerous social images have been emerging on the Web. How to precisely label these images is critical to image retrieval. However, traditional image-level tagging methods may become less effective because global image matching approaches can hardly cope with the diversity and arbitrariness of Web image content. This raises an urgent need for the fine-grained tagging schemes. In this work, we study how to establish mapping between tags and image regions, i.e. localize tags to image regions, so as to better depict and index the content of images. We propose the spatial group sparse coding (SGSC) by extending the robust encoding ability of group sparse coding with spatial correlations among training regions. We present spatial correlations in a two-dimensional image space and design group-specific spatial kernels to produce a more interpretable regularizer. Further we propose a joint version of the SGSC model which is able to simultaneously encode a group of intrinsically related regions within a test image. An effective algorithm is developed to optimize the objective function of the Joint SGSC. The tag localization task is conducted by propagating tags from sparsely selected groups of regions to the target regions according to the reconstruction coefficients. Extensive experiments on three public image datasets illustrate that our proposed models achieve great performance improvements over the state-of-the-art method in the tag localization task.

关键词： region of an image images sparse coding image retrieval

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