nils-herrmann/clean_scirepeval huggingface dataset:
A New Anchor-Labeling Method For Oriented Text Detection Using Dense Detection Framework

This letter proposes a new method for dense scene text detection anchor box labeling using single-shot multibox detection (SSD) as the base framework and VGG16 as the backbone, enhanced for scene text detection. This method can be further generalized to other detection tasks with various aspect ratios. We argue that the IoU criterion used by the dense object detection framework may have low recall ratios in extreme aspect ratio cases and oriented objects, and we propose a new criterion of the anchor-labeling method for these kinds of objects. The result shows that this method has better performance on public datasets compared with the previous labeling methods.
Self-enhanced relevant component analysis with side-information and unlabeled data

Relevant component analysis (RCA) is a powerful tool for relevant linear feature extraction with side-information, a new focus in machine learning fields. But its only utilizing positive constraints weakens this algorithm's performance and robustness, especially when there are few positive constraints - a common case in practice. To overcome this drawback, in this paper we propose an extended algorithm named self-enhanced relevant component analysis (SERCA). Through a boosting procedure in the product space, it efficiently uses both the given side-information and unlabeled data. The experimental results on several data sets show that SERCA achieves an obvious improvement compared with RCA.
A Real-Time Hand Pose Estimation System with Retrieval

In this paper, we propose a real-time system of hand pose estimation that infers the hand pose and shows the finger positions in each frame. The system is designed through data driven methodology. For the system to perform in real-time, we employ a retrieval method based on an inverted-file index with edge-based descriptors. To strengthen the discriminability, we combine a robust orientation assignment method with the descriptors. A novel alignment method is designed to transfer pose information from the retrieval results to the recognized image. To refine the retrieval results in the measurement of pose similarity, a mixed criterion that considers both outline deviation and descriptor matching is also proposed. The proposed system is quite scalable with respect to poses, since unobserved poses can be added freely. To evaluate the retrieval performance, we apply criteria that assess about the deviation of the corresponding positions of the query pose from the retrieval results. The evaluation is carried out on a retrieval dataset with manually marked images. Our proposed method effectively estimates pose at 20 frames per second, as demonstrated in our experiments on both real and publicly available datasets.
Multi-Pitch Estimation Based on Partial Event and Support Transfer

This paper proposes a method for the multi-pitch estimation of polyphonic music signals. Instead of on the frame level, the estimation is based on the partial event, which is defined like the note event in MIDI. All partial events in a piece of music are extracted dynamically in the process of the frame by frame short time Fourier transform (STFT). For each event, net support degree received from other events is calculated and the events with the highest support degrees are selected to be the fundamental frequency (FO) events. From another point of view, the support is transferred from higher frequency partial events to lower ones and finally concentrated on the FO events. This method can estimate the number of concurrent sounds, the onset and offset times of the notes. Experiments on both randomly mixed chord signals and synthesized ensemble music signals in "wav" format are conducted and the results are promising.
Unsupervised Large Margin Discriminative Projection

We propose a new dimensionality reduction method called maximum margin projection (MMP), which aims to project data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MMP projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MMP only depends on the geometry of the optimal decision boundary and not on the distribution of those data points lying further away from this boundary. Technically, MMP is formulated as an integer programming problem and we propose a column generation algorithm to solve it. Moreover, through a combination of theoretical results and empirical observations we show that the computation time needed for MMP can be treated as linear in the dataset size. Experimental results on both toy and real-world datasets demonstrate the effectiveness of MMP.
A novel algorithm for rotated human face detection

We present a framework to detect human faces rotated within the image plane. A novel orientation histogram of an image is constructed using local orientation analysis. From the view of an oriented pattern, a histogram of the human face shows symmetry with respect to the orientation, /spl beta/, of the principal axis, and a local peak also occurs at the orientation orthogonal to /spl beta/. We analyze the histogram to find the orientation of the face principal axis, with which conventional upright face detectors can be used to verify the supposed rotated face. Experimental results show that the algorithm is effective.
Scalable Image Coding Based on Epitomes

In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that the significant improvement of the rate-distortion performances can be achieved compared with the Scalable extension of HEVC (SHVC).
Learning an adaptive dictionary structure for efficient image sparse coding

We introduce a new method to learn an adaptive dictionary structure suitable for efficient coding of sparse representations. The method is validated in a context of satellite image compression. The dictionary structure adapts itself during the learning to the training data and can be seen as a tree structure whose branches are progressively pruned depending on their usage rate and merged into a single branch. This adaptive structure allows a fast search for the atoms and an efficient coding of their indices. It is also scalable in sparsity, meaning that once learned, the structure can be used for several sparsity values. We show experimentally that this adaptive structure offers better rate-distortion performances than the “flat” K-SVD dictionary, a dictionary structured in one branch, and the tree-structured K-SVD dictionary (called Tree K-SVD).
A subspace based method for error correction with DFT codes

A subspace based method for real error correction with low-pass DFT codes is proposed. The common roots of the eigen-polynomials of the syndrome covariance matrix associated with the zero eigenvalue gives the error locations. When the codevectors are quantized, the localization algorithm is modified so as to minimize the quantization noise effects. The proposed method is analogous to the subspace based spectral estimation techniques in array signal processing, but adapted to discrete error locations and only one set of syndrome coefficients. By combining the analytical results for perfect error localization with the subspace based approach, we obtain superior results than the existing approach.
Joint source-channel turbo decoding of VLC-coded Markov sources

We analyse the dependencies between the variables involved in the source and channel coding chain. This chain is composed of (1) a Markov source of symbols, followed by (2) a variable length source coder, and (3) a channel coder. The output process is analysed in the framework of Bayesian networks, which provide both an intuitive representation of the structure of dependencies, and a way of deriving joint (soft) decoding algorithms. Joint decoding relying on the hidden Markov model (HMM) of the global coding chain is intractable, except in trivial cases, due to the high dimensionality of the state space. We advocate instead an iterative procedure inspired from serial turbo codes, in which the three models of the coding chain are used alternately. This idea of using separately each factor of a big product model inside an iterative procedure usually requires the presence of an interleaver between successive components. We show that only one interleaver is necessary here, placed between the source coder and the channel coder. As a sub-product, we also derive a soft VLC decoder with good (and adjustable) synchronization properties.
Image prediction: Template matching vs. sparse approximation

The paper compares a sparse approximation based spatial texture prediction method with the template matching based prediction. Template matching algorithms have been widely considered for image prediction. These approaches rely on the assumption that the predicted texture contains a similar textural structure with the template in the sense of a simple distance metric between template and candidate. However, in real images, there are more complex textured areas where template matching fails. The basic idea instead is to consider sparse approximation algorithms. The proposed sparse spatial prediction is assessed against the prediction method based on template matching with a static and optimized dynamic templates. The spatial prediction method is then assessed in a coding scheme where the prediction residue is encoded with a coding approach similar to JPEG. Experimental observations show that the proposed method outperforms the conventional template matching based prediction.
Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary

We introduce a new image coder which uses the Iteration Tuned and Aligned Dictionary (ITAD) as a transform to code image blocks taken over a regular grid. We establish experimentally that the ITAD structure results in lower-complexity representations that enjoy greater sparsity when compared to other recent dictionary structures. We show that this superior sparsity can be exploited successfully for compressing images belonging to specific classes of images (e.g., facial images). We further propose a global rate-distortion criterion that distributes the code bits across the various image blocks. Our evaluation shows that the proposed ITAD codec can outperform JPEG2000 by more than 2 dB at 0.25 bpp and by 0.5 dB at 0.45 bpp, accordingly producing qualitatively better reconstructions.
Sparse optimization with directional DCT bases for image compression

This paper proposes a new compression algorithm based on the directional DCT (DDCT) bases introduced in. We first explain how to extend the DDCT concept to rectangular bases and exploit them to build a set of bases using a bintree segmentation. We then use dynamic programming to select a basis from this set according to a rate-distortion criterion. Comparisons in terms of rate-distortion performance are finally made with the current compression standards JPEG and JPEG2000.
A para-pseudo inverse based method for reconstruction of filter bank frame-expanded signals from erasures

Packet losses due to congestion or buffer overflows is a common problem in packet switched networks. Current network protocols manage this problem by retransmitting the lost packets. However, the delay due to the retransmission of the lost packets may be unacceptable for many real-time applications. Recent focus to resolve this problem is to recover the lost data from the received packets using some error control coding scheme. In this context, signal representation using frames has gained attention and has been studied in J. Kovacevic et al. (2002), P.L. Dragotti et al. (2001), G. Rath and C. Guillemot (2004), and R. Motwani and C. Guillemot (2004). Oversampled transforms and oversampled filter banks have been considered as joint-source channel codes and methods for reconstructing from erasures is studied in these articles. However, for oversampled filter banks, the reconstruction methods based on operating the pseudo-inverse based on the entire signal length are computationally complex and those based on reconstructing the erasures are not optimal as far as reconstruction mean square error is concerned. In this paper, we propose a method for reconstruction from erasures using a synthesis filter bank which functions as a pseudo-inverse. Hence, the scheme minimizes the reconstruction mean square error. Further, the method is computationally efficient, because it does not operate the pseudo-inverse corresponding to the entire signal vector. The synthesis filter bank, which obviously depends on the erasure pattern implements the pseudo-inverse at a practical computational cost. Some typical bursty erasure patterns which permit existence of a FIR synthesis filter banks are studied. The theoretical results are validated for bursty erasure patterns by simulations using image data.
Image prediction based on non-negative matrix factorization

This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative texture prediction methods have recently been considered for image prediction. These approaches rely on the assumption that the given basis functions (atoms) span the signal residue space at each iteration of the algorithm. However, in the case of signal prediction with a support region approximation, the atoms may not approximate residue signals very well even though the dictionary has been well adapted in the spatial domain. The underlying main idea is to consider a factorization based algorithm in which the given atoms approximate the signal without going further into signal residue space. The proposed spatial prediction method has first been assessed against the prediction methods based on template matching and sparse approximations. It has then been assessed in a compression scheme where the prediction residue is transform encoded. Experimental results obtained show that the proposed method outperforms the template matching and sparse approximations based techniques in terms of encoding efficiency.
Maximum Likelihood BSC Parameter Estimation for the Slepian-Wolf Problem

In the context of Distributed Source Coding, we propose a low complexity algorithm for the estimation of the cross-over probability p of the Binary Symmetric Channel (BSC) modeling the correlation between two binary sources. The coding is done with linear block codes. We propose a novel method to estimate p prior to decoding and show that it is the Maximum Likelihood estimator of p with respect to the syndromes of the correlated sources. The method can be utilized for the parameter estimation for channel coding of binary sources over the BSC.
Approximate nearest neighbors using sparse representations

A new method is introduced that makes use of sparse image representations to search for approximate nearest neighbors (ANN) under the normalized inner-product distance. The approach relies on the construction of a new sparse vector designed to approximate the normalized inner-product between underlying signal vectors. The resulting ANN search algorithm shows significant improvement compared to querying with the original sparse vectors. The system makes use of a proposed transform that succeeds in uniformly distributing the input dataset on the unit sphere while preserving relative angular distances.
Hidden Markov Model for distributed video coding

This paper addresses the problem of asymmetric distributed coding of correlated binary Hidden Markov Sources, modeled as a Gilbert-Elliott process. The model parameters are estimated with an estimation-decoding Expectation-Maximization algorithm. The rate gain obtained by accounting for the memory of the sources is first assessed theoretically. The method is then shown to improve the PSNR versus rate performance of a Distributed Video Coding system, based on Low-Density Parity-Check codes.
K-WEB: Nonnegative dictionary learning for sparse image representations

This paper presents a new nonnegative dictionary learning method, to decompose an input data matrix into a dictionary of nonnegative atoms, and a representation matrix with a strict ℓ0-sparsity constraint. This constraint makes each input vector representable by a limited combination of atoms. The proposed method consists of two steps which are alternatively iterated: a sparse coding and a dictionary update stage. As for the dictionary update, an original method is proposed, which we call K-WEB, as it involves the computation of k WEighted Barycenters. The so designed algorithm is shown to outperform other methods in the literature that address the same learning problem, in different applications, and both with synthetic and “real” data, i.e. coming from natural images.
Optimal Distributed Linear Transceivers for Sending Independently Corrupted Copies of a Colored Source Over the Gaussian MAC

We consider distributed linear transceivers for sending a second-order wide-sense stationary process observed by two noisy sensors over a Gaussian multiple-access channel (MAC). We derive the minimum mean-square error (MSE) distributed linear transceiver. The optimal linear transmitter exploits bandwidth expansion by repeating transmission and the transmitters at the two sensors are the same except for a constant factor. When the source is white, encoded transmission is the best linear code for any SNR. But for a colored source, whitening transmit filter is sub-optimal. In high SNR regime, the magnitude response of the optimal transmission filter is inversely proportional to fourth-root of the power spectrum of the process (while that for the whitening filler is inversely proportional to the square-root of the spectrum). In the special case of n single sensor with Gaussian source, we also quantify the performance loss of linear source-channel codes with respect to the Shannon limit.
Joint source-channel decoding of quasiarithmetic codes

This paper addresses the issue of robust and joint source-channel decoding of quasiarithmetic codes. Quasiarithmetic coding is a reduced precision and complexity implementation of arithmetic coding. This paper provides first a state model of a quasiarithmetic decoder for binary and M-ary sources. The design of an error-resilient soft decoding algorithm follows quite naturally. The compression efficiency of quasiarithmetic codes allows to add extra redundancy in the form of markers designed specifically to prevent de-synchronization. The algorithm is directly amenable for iterative source-channel decoding in the spirit of serial turbo codes. The coding and decoding algorithms have been tested for a wide range of channel signal-to-noise ratios. Experimental results reveal improved SER and SNR performances against Huffman and optimal arithmetic codes.
Application of DFT codes for robustness to erasures

Discrete Fourier transform (DFT) codes are used to provide robustness against packet losses in IP networks. First, the relationship between DFT codes and over-complete expansions using frames is established. It is shown that the message recovery by syndrome decoding of erasures and direct signal space projection from received samples are equivalent. The error performance analysis of DFT codes is used to develop packetization criteria in order to guarantee minimum reconstruction error.
Oriented Wavelet Transform for Image Compression and Denoising

In this paper, we introduce a new transform for image processing, based on wavelets and the lifting paradigm. The lifting steps of a unidimensional wavelet are applied along a local orientation defined on a quincunx sampling grid. To maximize energy compaction, the orientation minimizing the prediction error is chosen adaptively. A fine-grained multiscale analysis is provided by iterating the decomposition on the low-frequency band. In the context of image compression, the multiresolution orientation map is coded using a quad tree. The rate allocation between the orientation map and wavelet coefficients is jointly optimized in a rate-distortion sense. For image denoising, a Markov model is used to extract the orientations from the noisy image. As long as the map is sufficiently homogeneous, interesting properties of the original wavelet are preserved such as regularity and orthogonality. Perfect reconstruction is ensured by the reversibility of the lifting scheme. The mutual information between the wavelet coefficients is studied and compared to the one observed with a separable wavelet transform. The rate-distortion performance of this new transform is evaluated for image coding using state-of-the-art subband coders. Its performance in a denoising application is also assessed against the performance obtained with other transforms or denoising methods
Video super-resolution via sparse combinations of key-frame patches in a compression context

In this paper we present a super-resolution (SR) method for upscaling low-resolution (LR) video sequences, that relies on the presence of periodic high-resolution (HR) key frames, and validate it in the context of video compression. For a given LR intermediate frame, the HR details are retrieved patch-by-patch by taking sparse linear combinations of patches found in the neighbor key frames. The performance of the video SR algorithm is assessed in a scheme where only some key frames from an original HR sequence are directly encoded; the remaining intermediate frames are down-sampled to LR and encoded as well, with a possibly different quantization parameter. SR is then finally employed to upscale these frames. For comparison, we consider the best case where the whole original HR sequence is encoded. With respect to this case, our SR-based approach is shown to bring a certain gain for low bit-rates (consistent when all frames are encoded independently), i.e. when a poor encoding can actually benefit of the special processing of the intermediate frames, so proving that video SR can be an useful tool in realistic scenarios.
Homography-based low rank approximation of light fields for compression

This paper studies the problem of low rank approximation of light fields for compression. A homography-based approximation method is proposed which jointly searches for homographies to align the different views of the light field together with the low rank approximation matrices. We first consider a global homography per view and show that depending on the variance of the disparity across views, the global homography is not sufficient to well-align the entire images. In a second step, we thus consider multiple homographies, one per region, the region being extracted using depth information. We first show the benefit of the joint optimization of the homographies together with the low-rank approximation. The resulting compact representation is then compressed using HEVC and the results are compared with those obtained by directly applying HEVC on the light field views re-structured as a video sequence. The experiments using different data sets show substantial PSNR-rate gain of our method, especially for real light fields.