nils-herrmann/clean_scirepeval huggingface dataset:
Top Downloads in IEEE Xplore [Reader's Choice]

Presents a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most downloaded IEEE Xplore articles. We decided to try some different things to increase the column's utility to the reader. This month's column focuses on articles in IEEE Tranasctions on Image Processing. Future issues will look at the top downloads from other SPS publications. We hope this will increase the number of different articles/papers we highlight, in part because we are guaranteed not to repeat from issue to issue, but also because journals with a focused readership will still have their top downloads highlighted.
An overview of JPEG-2000

JPEG-2000 is an emerging standard for still image compression. This paper provides a brief history of the JPEG-2000 standardization process, an overview of the standard, and some description of the capabilities provided by the standard. Part I of the JPEG-2000 standard specifies the minimum compliant decoder, while Part II describes optional, value-added extensions. Although the standard specifies only the decoder and bitstream syntax, in this paper we describe JPEG-2000 from the point of view of encoding. We take this approach, as we believe it is more amenable to a compact description more easily understood by most readers.
Icon based error concealment for JPEG and JPEG 2000 images

This paper describes methods to recover the useful data in JPEG and JPEG 2000 compressed images and to estimate data for those portions of the image where correct data cannot be recovered. These techniques are designed to handle the loss of hundreds of bytes in the file. No use is made of restart markers or other optional error detection features of JPEG and JPEG 2000, but an uncorrupted low resolution version of the image, such as an icon, is assumed to be available. These icons are typically present in Exif or JFIF format JPEG files.
Top Downloads in IEEE Xplore [Reader's Choice]

This issue?s ?Reader?s Choice? column contains a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most downloaded IEEE Xplore articles through June 2013. The table below contains the citation information for each article and the rank obtained in IEEE Xplore. The highest rank obtained by an article in this time frame is indicated in bold. Your suggestions and comments are welcome and should be sent to Associate Editor Michael Gormish (gormish@ieee.org).
Optimal Display Adaptation of Iconic Document Visualizations via BFOS-Style Tree Pruning

This paper introduces a new visual representation of a document or group of documents, a dynamic document icon, or Dydocon. Its representation is symbolic like an icon, but changes depending on document content. A Dydocon can be used for multiple documents and thus is useful for presentation of clustered search results. Because a large number of clusters may be returned by a search it is desirable to optimally select clusters given available display space. We cluster documents into a visual taxonomy described by a tree of Dydocons, define tree functionals for icon size and distortion of a cluster, and perform display-size adaptation through optimization using a BFOS-style algorithm.
A new wavelet-based approach to sharpening and smoothing of images in Besov spaces with applications to deblurring

The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, state-of-the-art denoising techniques use wavelet bases instead of Fourier bases since wavelet domain processing provides local adaptation in smooth and non-smooth parts due to the theoretical link between wavelets and smoothness spaces. In this paper the same smoothness spaces are used to propose a way of performing sharpening and smoothing of signals with wavelets (WSS) in Besov spaces. As an application the completely wavelet-based enhancement of a scanned document is discussed.
Top Downloads in IEEE Xplore [Reader's Choice]

Each issue of the "Reader's Choice" column contains a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most downloaded IEEE Xplore articles. This issue is based on download data through August 2012. The table provided contains the citation information for each article and the rank obtained in IEEE Xplore. The highest rank obtained by an article in this time frame is indicated in bold.
A comparison of computational techniques of network analysis

Efficient estimates of delay are developed and incorporated into the general local area network analysis system (GLAS). This hybrid estimate is based on analytical calculations of service time and overall interarrivals to the network, and measurements of network idle cycles. At network loads varying from 0.5 to 0.95, the method provides an improvement in the confidence interval width of between one and 34. This confidence interval improvement results in a simulation time decrease of up to three orders of magnitude.<<ETX>>
Very high speed entropy coding

Efforts to build high-speed hardware for many different entropy coders are limited by fundamental feedback loops. A method that allows for parallel compression in hardware is described. This parallelism results in extremely high rates, 100 million symbols/second or higher. The system is generalizable to any lossless or lossy system with deterministic decompression. Prototype hardware that divides the data into multiple streams that feed parallel coders is presented. The problem of efficient transmission of multiple streams of variable-length coded data is solved by a unique coded data interleave method.<<ETX>>
JPEG 2000: worth the wait?

The seeds of JPEG 2000 were planted at a meeting of the JPEG committee (ISO/IEC JTC1/SC29 WG1) in 1995. The eventual standard should provide new ways to deal with images in compressed format. Although the "requirements" for the current standard are extensive, essentially the standard will allow an image to be compressed once (losslessly if desired) and different sub-bitstreams extracted to meet the requirements of the application (monochrome, reduced resolution, region of interest, progressive display, even transmission over error prone channels). Unfortunately, this "work item" will not become a full fledged international Standard until at least 2001. This paper discusses the history of JPEG 2000, the technologies in the current verification model (color and wavelet transforms, context models, entropy coder, quantization techniques, region or interest, error resilience) and how these technologies work together to achieve features desired in a modem compression system. A status report for current JPEG committee activities and schedule is included.
Top Downloads in IEEE Xplore [Reader's Choice]

Top Downloads in IEEE Xplore T he " Reader's Choice " column in IEEE Signal Processing Magazine contains a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most down-loaded IEEE Xplore articles. This issue is based on download data through June 2012. The table below contains the citation information for each article and the rank obtained in IEEE Xplore. The highest rank obtained by an article in this time frame is indicated in bold. Your suggestions and comments are welcome and should be sent to Associate Editor Michael Gormish (gormish@ieee.org).
The transport of reversible and unreversible embedded wavelets (TRUEW)

Transport of reversible and unreversible embedded wavelets (TRUEW) is a system for the portion delivery of compressed JPEG 2000 files over a network. A discussion on the development of the TRUEW architecture, the benefits of the TRUEW architecture, an efficiency analysis of tile-part and precint access to JPEG 2000 compressed images, and some comments on potential applications of interactive image access are presented. Computations show that for an example access pattern, TRUEW provides comparable performance in terms of disk accesses and data transferred with more complex precinct based systems.
Top Downloads in IEEE Xplore [Reader's Choice]

Top Downloads in IEEE Xplore T he " Reader's Choice " column in IEEE Signal Processing Magazine contains a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most downloaded IEEE Xplore articles. This issue is based on download data through August 2013. The table below contains the citation information for each article and the rank obtained in IEEE Xplore. The highest rank obtained by an article in this time frame is indicated in bold. Your suggestions and comments are welcome and should be sent to Associate Editor Michael Gormish (gormish@ieee.org). This paper reviews optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Variants of the particle filter are introduced within a framework of the sequential importance sampling (SIS) algorithm and compared with the standard EKF. This paper extracts overlapping image patches, orders these patches and applies one dimensional filtering to the reordered set of pixels. These techniques are applied to denoising and inpainting. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. Existing and new algorithms for assigning anonymous IDs are examined with respect to communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. This paper introduces a framework for quality assessment based on the degradation of structural information. Within this framework a structure similarity index is developed and evaluated. MATLAB code available.
Unsupervised Domain Adaptation With Label and Structural Consistency

Unsupervised domain adaptation deals with scenarios in which labeled data are available in the source domain, but only unlabeled data can be observed in the target domain. Since the classifiers trained by source-domain data would not be expected to generalize well in the target domain, how to transfer the label information from source to target-domain data is a challenging task. A common technique for unsupervised domain adaptation is to match cross-domain data distributions, so that the domain and distribution differences can be suppressed. In this paper, we propose to utilize the label information inferred from the source domain, while the structural information of the unlabeled target-domain data will be jointly exploited for adaptation purposes. Our proposed model not only reduces the distribution mismatch between domains, improved recognition of target-domain data can be achieved simultaneously. In the experiments, we will show that our approach performs favorably against the state-of-the-art unsupervised domain adaptation methods on benchmark data sets. We will also provide convergence, sensitivity, and robustness analysis, which support the use of our model for cross-domain classification.
Hierarchical K-means Clustering Using New Support Vector Machines for Multi-class Classification

We propose a binary hierarchical classification structure to address the multi-class classification problem with a new hierarchical design method, k-means SVRM (support vector representation machine) clustering. This greatly improves upon our prior IJCNN hierarchical design. At each node in the hierarchy, we apply the SVRDM (support vector representation and discrimination machine) classifier, which offers generalization and good rejection ability. We also provide new theoretical bases and methods for our choice of the kernel function and new SVRDM parameter selection rules. Classification and rejection test results are presented on new databases of both simulated and real infra-red (IR) data.
Automatic saliency inspired foreground object extraction from videos

In this paper, we propose a saliency inspired video object extraction (VOE) method to extract and segment foreground objects of interest from videos captured by freely moving cameras. Our method aims at detecting visual and motion salient regions from an input video, and thus we integrate such cosaliency information with the associated foreground and background color models to achieve VOE. A conditional random field (CRF) is applied in our framework to automatically identify the foreground object regions based on the above features, while our method does not need any prior knowledge on the foreground objects of interest or any interaction from the users. Experiments on a variety of videos confirm that our method is able to provide quantitatively and qualitatively more satisfactory results when comparing to state-of-the-art VOE approaches.
Locality-constrained group sparse representation for robust face recognition

This paper presents a novel sparse representation for robust face recognition. We advance both group sparsity and data locality and formulate a unified optimization framework, which produces a locality and group sensitive sparse representation (LGSR) for improved recognition. Empirical results confirm that our LGSR not only outperforms state-of-the-art sparse coding based image classification methods, our approach is robust to variations such as lighting, pose, and facial details (glasses or not), which are typically seen in real-world face recognition problems.
Heterogeneous domain adaptation with label and structure consistency

Domain adaptation is a challenging task, since it associates data collected from different domains or exhibiting distinct distributions. In this paper, we particularly focus on adapting cross-domain data with distinct feature dimensions or representations. Thus, this is referred to as the task of heterogeneous domain adaptation (HDA). To solve HDA, we propose Label and Structure-consistent Unilateral Projection (LS-UP) that transforms source-domain data to the target domain, with the goal of matching cross-domain data distribution and preserving data structure after projection. The main contribution of our work is its ability in relating cross-domain data with different feature representations. We evaluate our LS-UP for HDA on two different cross-domain classification problems, and we show that our method would perform favorably against state-of-the-art approaches.
Learning Grassmann manifolds for object state discovery

In this paper, we advocate the use of Grassmann manifolds for discovering object images in different states (e.g., unripe, peeled, etc.). We propose a novel dictionary learning algorithm, which derives the subspaces on a Grassmann manifold for describing each object state. By our introduced geodesic-flow constraint, our Grassmann manifold exhibits excellent capabilities in relating objects in distinct states (i.e., subspaces on the derived manifold), while the geodesics connecting different states can be viewed as transformations between the associated states. This is the reason why the use of our proposed Grassmann manifold can be applied to perform object state classification with improved performance. In our experiments, we provide quantitative and qualitative results to verify the effectiveness of our proposed method.
Robust Face Recognition With Structurally Incoherent Low-Rank Matrix Decomposition

For the task of robust face recognition, we particularly focus on the scenario in which training and test image data are corrupted due to occlusion or disguise. Prior standard face recognition methods like Eigenfaces or state-of-the-art approaches such as sparse representation-based classification did not consider possible contamination of data during training, and thus their recognition performance on corrupted test data would be degraded. In this paper, we propose a novel face recognition algorithm based on low-rank matrix decomposition to address the aforementioned problem. Besides the capability of decomposing raw training data into a set of representative bases for better modeling the face images, we introduce a constraint of structural incoherence into the proposed algorithm, which enforces the bases learned for different classes to be as independent as possible. As a result, additional discriminating ability is added to the derived base matrices for improved recognition performance. Experimental results on different face databases with a variety of variations verify the effectiveness and robustness of our proposed method.
Learning graph fusion for query and database specific image retrieval

In this paper, we propose a graph-based image retrieval algorithm via query and database specific feature fusion. While existing feature fusion approaches exist for image retrieval, they typically do not consider the image database of interest (i.e., to be retrieved) for observing the associated feature contributions. In the offline learning stage, our proposed method first identifies representative features for describing images to be retrieved. Given a query input, we further exploit and integrate its visual information and utilize graph-based fusion for performing query-database specific retrieval. In our experiments, we show that our proposed method achieves promising performance on the benchmark database of UKbench, and performs favorably against recent fusion-based image retrieval approaches.
Person-specific domain adaptation with applications to heterogeneous face recognition

Heterogeneous face recognition (HFR) is a practical yet challenging task in which gallery and probe face images are collected in terms of different modalities or features (e.g., sketch vs. photo). In this paper, we present a person-specific domain adaptation framework for HFR. By utilizing the subjects not of interest (i.e., those not to be recognized), we first derive a common feature space using their cross-domain face images, with the goal of eliminating differences between image modalities. To generalize our feature space for representing and recognizing the subjects of interest, we advocate the construction of person-specific domain adaptation model in this space, so that the classifiers (trained by the gallery images) are able to achieve satisfactory recognition performance. In our experiments, we consider sketch-to-photo and near-infrared (NIR) to visible spectrum (VIS) face recognition problems for evaluating the effectiveness of our method.
Self-Learning of Edge-Preserving Single Image Super-Resolution via Contourlet Transform

We present a self-learning approach for single image super-resolution (SR), with the ability to preserve high frequency components such as edges in resulting high resolution (HR) images. Given a low-resolution (LR) input image, we construct its image pyramid and produce a super pixel dataset. By extracting context information from the super-pixels, we propose to deploy context-specific contour let transform on them in order to model the relationship (via support vector regression) between the input patches and their associated directional high-frequency responses. These learned models are applied to predict the SR output with satisfactory quality. Unlike prior learning-based SR methods, our approach advances a self-learning technique and does not require the self similarity of image patches within or across image scales. More importantly, we do not need to collect training LR/HR image data in advance and only require a single LR input image. Empirical results verify the effectiveness of our approach, which quantitatively and qualitatively outperforms existing interpolation or learning-based SR methods.
Group lasso regularized multiple kernel learning for heterogeneous feature selection

We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature selection. We extend the existing MKL algorithm and impose a mixed ℓ1 and ℓ2 norm constraint (known as group lasso) as the regularizer. Our GL-MKL determines the optimal base kernels, including the associated weights and kernel parameters, and results in a compact set of features for comparable or improved recognition performance. The use of our GL-MKL avoids the problem of choosing the proper technique to normalize the feature attributes collected from heterogeneous domains (and thus with different properties and distribution ranges). Our approach does not need to exhaustively search for the entire feature space when performing feature selection like prior sequential-based feature selection methods did, and we do not require any prior knowledge on the optimal size of the feature subset either. Comparisons with existing MKL or sequential-based feature selection methods on a variety of datasets confirm the effectiveness of our method in selecting a compact feature subset for comparable or improved classification performance.
Extended-bag-of-features for translation, rotation, and scale-invariant image retrieval

While bag-of-features (BOF) models have been widely applied for addressing image retrieval problems, the resulting performance is typically limited due to its disregard of spatial information of local image descriptors (and the associated visual words). In this paper, we present a novel spatial pooling scheme, called extended bag-of-features (EBOF), for solving the above task. Besides improving image representation capability, the incorporation of the our EBOF model with a proposed circular-correlation based similarity measure allows us to perform translation, rotation, and scale-invariant image retrieval. We conduct experiments on two benchmark image datasets, and the performance confirms the effectiveness and robustness of our proposed approach.
Graph regularized low-rank matrix recovery for robust person re-identification

Robust person re-identification (PRID) refers to the problem of matching individuals across non-overlapping camera views, while the images captured by either camera might be occluded or even missing. To address this challenging task, we propose a low-rank matrix recovery (LR) based approach in this paper. In addition to observing the global structure of cross-camera images via LR, we further exploit their local geometrical information via graph regularization, which preserves the recovered images with recognition guarantees. Our experiments verify the effectiveness and robustness of our approach, which is shown to perform favorably against state-of-the-art PRID methods.
Exploiting low-rank structures from cross-camera images for robust person re-identification

Matching individuals across non-overlapping camera views is known as the problem of person re-identification. In addition to significant visual appearance variations due to lighting, view angle, etc. changes, one might encounter corrupted data due to background clutter and occlusion, or even missing data at some camera views in practical scenarios. To address the above challenges, we present a novel approach to robust person re-identification, particularly aiming at handling missing and corrupted image data across camera views. Based on the technique of low-rank matrix decomposition, our proposed algorithm observes the low-rank structure of cross-view data, which is able to disregard extreme/sparse errors while the missing instances can be recovered automatically. Our experiments will confirm the effectiveness and robustness of our method, which is shown to outperform several baseline and state-of-the-art person re-identification approaches.