nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Layered Multicast with Source Rate Adaption in Two-Level Hierarchical Overlay Networks

Thanks to recent advances in hierarchical video coding and overlay network, layered overlay multicast has emerged as an important solution for video streaming over heterogeneous network. In this paper, we formulate it into a joint network flow control and performance optimization problem where adaptive layer rates are determined through a greedy algorithm close to optimal. The overlay network is constrained by a practical two-level hierarchical overlay model where the bandwidth sharing is imposed on underlying edge-bottleneck links. To maintain less computational complexity and avoidance of global information, the $M$-layer maximization problem is transformed into multiple one-layer minimization subproblems. In turn, a fully distributed algorithm is developed by decomposing the one-layer minimization problem into both inequality constrained transportation subproblem and the shortest path subproblem by using Lagrangian duality. We demonstrate that the distributed algorithm converges quickly and attains effective performance in both static and dynamic networks.
A structured learning-based graph matching for dynamic multiple object tracking

To correctly detect dynamic targets and obtain a record of the trajectories of identical targets in appearance over time, has become significantly more challenging and infers countless applications in biomedicine. In this paper, we propose a novel structured learning-based graph matching algorithm to track a variable number of interacting objects in dynamic environments. Different from previous approaches, the proposed method takes full advantage of neighboring relationships as edge feature in the structured graph. The target problem is regarded as structured node and edge matching between graphs generated from successive frames. In essence, it is formulated as the maximum weighted bipartite matching problem which is solved by dynamic Hungarian algorithm. The parameters of the structured graph matching model can be acquired in a stochastic graduated learning step in different dynamic environments. The extensive experiments on dynamic cell and football sequences demonstrate that the resulting approach deals effectively with complicated target interactions.
Semi-supervised object recognition using structure kernel

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels.
Extended conjugate polar fourier transforminconvolution network

This paper proposes an extended conjugate polar Fourier transform (ECPFT), to design iterated radial filter bank (RFB) and directional filter bank (DFB) convenient for accurate multiscale and multidirectional decomposition in discretization over a convolution network. With conjugated symmetric form, ECPFT would convert complex directional wavelets in original spatial domain to real ones in the inverse Fourier domain of ECPFT. Furthermore, it can contribute to changing the design of nonseparable RFB and DFB to decomposition in scales and directions with 1-D filter bank in each dimension separably in the ECPFT domain. The generated properties of conjugate symmetry and periodicity of 2π in both angular and radial dimensions, also guarantee convolution and sampling operations in both dimensions within the inverse Fourier domain of ECPFT. The fast algorithm for discretization is developed to reduce complexity.
An Adaptive Difference Distribution-Based Coding with Hierarchical Tree Structure for DNA Sequence Compression

Previous reference-based compression on DNA sequences do not fully exploit the intrinsic statistics by merely concerning the approximate matches. In this paper, an adaptive difference distribution-based coding framework is proposed by the fragments of nucleotides with a hierarchical tree structure. To keep the distribution of difference sequence from the reference and target sequences concentrated, the sub-fragment size and matching offset for predicting are flexible to the stepped size structure. The matching with approximate repeats in reference will be imposed with the Hamming-like weighted distance measure function in a local region closed to the current fragment, such that the accuracy of matching and the overhead of describing matching offset can be balanced. A well-designed coding scheme will make compact both the difference sequence and the additional parameters, e.g. sub-fragment size and matching offset. Experimental results show that the proposed scheme achieves 150% compression improvement in comparison with the best reference-based compressor GReEn.
Figure/ground video segmentation using greedy transductive cosegmentation

Cosegmentation has achieved great success in exploiting inter-image segmentation consistency to segment a group of images simultaneously. To enforces non-local temporal coherence across all the frames by high-order object-level appearance/semantic correspondence with a compensation to the short-time window motion coherence cue, this paper cosegments the video frames together with a novel interframe segmentation consistency term. A direct application of existing cosegmentation algorithms to video frames encounters the following challenges: the high correlation of adjacent frames which makes the segmentation ambiguous and a large number of video frames which makes the computation expensive. To tackle them, we formulate the cosegmentation in a transductive learning framework to iteratively learn the inter-frame consistency term from all the video frames. The proposed algorithm is evaluated on the standard SegTrack dataset and promising results are obtained.
Structure-Based Learning in Sampling, Representation and Analysis for Multimedia Big Data

This paper presents disruptive insights and techniques on structure-based learning for multimedia big data. Along this viewpoint, significant technical challenges for multimedia big data are investigated, including sampling and reconstruction, representation, and analysis. For multi-dimensional compressive sampling, the union of data-driven subspace is addressed via subspace learning with structured sparsity. To enrich the correlated reconstruction, spatio-temporal regularity is presented within various multimedia data. Inspired by this insight, multi-scale dictionary learning is proposed to leverage spatio-temporal structures for sparse representation and make learning-based structured prediction and analysis.
A generic video coding framework based on anisotropic diffusion and spatio-temporal completion

This paper develops a complete framework for perceptual video coding with anisotropic diffusion-based abstraction and completion under the spatio-temporal variation regularity. A soft clustering method is applied to retrieve transferable semantic implications of sampled frames in a sparser way. The restoration inference process as a learning equivalent optimization problem from a given set of sparse data, serves as non-parametric or exemplar-based sampling method by taking 3-D spatio-temporal similarity into consideration. Aside from pixel-wise color-based matching, two patch-based similarity metrics on motion-based pixel-wise similarity and semantic coherence are introduced for the exemplar-based reconstruction under complex situations. For each pixel in the abstracted frames, a set of matched patches will provide inference and its color is predicted with multi-hypothesis via weighted averaging. A pixel-wise confidence map based on spatio-temporal feature is also provided for important point selection so as to reduce the computation cost to an acceptable level. We validate both compression efficiency and restoration performance from coding gain, SSIM, optical flow consistency, and just-noticeable distortion (JND) on a variety of sources.
A joint rate control scheme for H.264 encoding of multiple video sequences

The objective of joint rate control is to dynamically distribute the channel capacity among video sequences according to their respective complexities, thus a more uniform picture quality and a more efficient utilization of channel capacity are achieved. Most existing approaches are based on MPEG2 coding platform. This paper presents a novel joint rate control scheme for multiple video sequences coding based on H.264 standard. The joint rate control is performed hierarchically from a group of pictures of all sequences down to each picture of every sequence, depending on both the complexity of frames and the buffer occupancy. A novel complexity measure that adapts to the characteristics of H.264 video coding is proposed. An improved joint buffer control strategy, which is extended from that in the single sequence rate control scheme, is applied. Experimental results show that the proposed scheme maintains a good balance in picture quality among the sequences as well as within a sequence.
Genome Sequence Compression with Distributed Source Coding

In this paper, we develop a novel genome compression framework based on distributed source coding (DSC)[3], which is specially tailored to the need of miniaturized devices. At the encoder side, subsequences with adaptive code length can be compressed flexibly through either low complexity DSC based syndrome coding or hash coding with the decision determined by the existence of variations between source and reference known from the decoder feedback. Moreover, to tackle the variations between source and reference at the decoder, we carefully designed a factor graph based low-density parity-check (LDPC) decoder, which automatically detects insertion, deletion and substitution.
Sparse Representation With Spatio-Temporal Online Dictionary Learning for Promising Video Coding.

Classical dictionary learning methods for video coding suffer from high computational complexity and interfered coding efficiency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3D low-frequency and high-frequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data, such as batch learning methods, e.g., K-SVD. Since the selected volumes are supposed to be independent identically distributed samples from the underlying distribution, decomposition coefficients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL-based coding scheme achieves performance improvements than H.264/AVC or High Efficiency Video Coding as well as existing super-resolution-based methods in rate-distortion performance and visual quality.
Visual preserving video retargeting with deformable shape consistency

Video retargeting is a technique that resizes the aspect ratio and resolution of a video in a content aware manner. There are three main challenges in the video retargeting: visual saliency preservation, deformation prevention and temporal consistency preservation. Existing video retargeting methods can be roughly classified into seam carving based methods and warping based methods. These methods effectively protect important regions in video, but can hardly deal with deformations and temporal incoherence, especially for videos whose foreground and background are both in motion. In this paper, we propose a novel shape preserving video retargeting method to reduce deformations and maintain temporal consistency via matching salient curves in the video frames. Our method transforms the anti-deformation and temporal consistency problems in video retargeting into a curve matching cost minimization problem. By incorporating a deformation cost and temporal inconsistency cost into the seam carving framework, the quality of the retargeted videos can be significantly improved. Experimental results also show that the proposed method achieves better performance compared with other two state-of-the-art methods.
Optimized inpainting-based macroblock prediction in video compression

In this paper, we propose an optimized inpainting-based macroblock prediction mode in the state-of-the-art H.264/AVC video engine. Parallel with the existing intra- and inter- modes, it is regularized by the global tempo-spatial consistency between the current MB and the co-located decoded region. The target is formulated as a local optimization problem by minimizing the energy of Markov Random Field (MRF). An ordered belief propagation (BP) is developed to solve the optimization problem with spatio-temporal consistency regularity and achieve the patch assignment of largest probability. It ensures a stable marginal belief distribution through updating local messages via iterative forward and backward process, to impose a prioritized inference on the structure. Rate-distortion optimization is used to evaluate the mode selection within the inpainting-based prediction mode, intra- and inter modes. It has been implemented into H.264/AVC, and achieves bit-rate saving and higher PSNR performance, especially in low bit-rate.
Multiscale Online Dictionary Learning for Quality Scalable Video Coding

This paper proposes a novel multiscale online dictionary learning algorithm with double sparsity structure for scalable video coding. Along hierarchical structures on the feature set by wavelet transform, the search space of online learning is optimized to sub-blocks for hierarchical sparsity. The group sparsity is exploited on lowest sub-band in the base layer to obtain the low-frequency sub-dictionary and sparse coefficient. We also designed cross-scale decomposition and reconstruction, for which the recovery error can be bounded. The dictionary is updated by stochastic gradient descent to optimize the expected cost. Hierarchical high-frequency information is predicted from a pre-learned corresponding sub-dictionary pairs for scalable coding. We demonstrated that the proposed algorithm can achieve scalable signal to noise ratio (SNR).
Incorporating Primal Sketch Based Learning Into Low Bit-Rate Image Compression

This paper proposes an image compression approach, in which we incorporate primal sketch based learning into the mainstream image compression framework. The key idea of our approach is to use primal sketch information to enhance the quality of distorted images. With this method, we only encode the down-sampled image and use the primal sketch based learning to recover the high frequency information which has been removed by down-sampling. Experimental results demonstrate that our scheme achieves better objective visual quality as well as subjective quality compared with JPEG2000 at the same bit-rates.
Gaussian Process Regression Based Prediction for Lossless Image Coding

LS-based adaptation cannot fully exploit high-dimensional correlations in image signals, as linear prediction model in the input space of supports is undesirable to capture higher order statistics. This paper proposes Gaussian process regression for prediction in lossless image coding. Incorporating kernel functions, the prediction support is projected into a high-dimensional feature space to fit the anisotropic and nonlinear image statistics. Instead of directly conditioned on the support, Gaussian process regression is leveraged to make prediction in the feature space. The model parameters are optimized by measuring the similarities based on the training set, which is evaluated by combined kernel function in the sense of translation and rotation invariance among supports mapped in the feature space. Experimental results show that the proposed predictor outperforms most benchmark predictors reported.
A New Deblocking Algorithm Based on Adjusted Contourlet Transform

A new postprocessing method based on adjusted contourlet transform is introduced in this paper for suppressing blocking artifacts (BA) in block-based discrete cosine transform (BDCT) compressed images. To our best knowledge, this is the first time contourlet is applied to this field. By exploiting scale space edge detector (ss-edge detector), our algorithm can extract and protect blocking map (BM) and edge map (EM) in the compressed image respectively in the same time. By transforming the compressed image into adjusted contourlet domain, the adaptive thresholds are obtained according to BM. According to the adaptive thresholds, the contourlet coefficients in different subbands are filtered. Experimental results show that our deblocking algorithm achieves better performance than the other iterative and noniterative methods reported in the literature
Picking Deep Filter Responses for Fine-Grained Image Recognition

Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts. Most previous works rely on object / part level annotations to build part-based representation, which is demanding in practical applications. This paper proposes an automatic fine-grained recognition approach which is free of any object / part annotation at both training and testing stages. Our method explores a unified framework based on two steps of deep filter response picking. The first picking step is to find distinctive filters which respond to specific patterns significantly and consistently, and learn a set of part detectors via iteratively alternating between new positive sample mining and part model retraining. The second picking step is to pool deep filter responses via spatially weighted combination of Fisher Vectors. We conditionally pick deep filter responses to encode them into the final representation, which considers the importance of filter responses themselves. Integrating all these techniques produces a much more powerful framework, and experiments conducted on CUB-200-2011 and Stanford Dogs demonstrate the superiority of our proposed algorithm over the existing methods.
Spatio-temporal coherence for 3-D view synthesis with curve-based disparity warping

View synthesis is dedicated to generating arbitrary views of the same scene from given inputs. As an alternative to depth-image-based rendering (DIBR), image warping based view synthesis approaches could automatically generate visually plausible virtual views in real-time. Recognizing that existing techniques would lead to temporal incoherence and shape distortions in synthesized videos, this paper proposes a novel video warping algorithm which motion saliency map and global motion from reference views are incorporated into motion-aware constraints to maintain temporal coherence in virtual views. Furthermore, a salient curve based disparity constraint is imposed to prevent shape deformations and avoid possible artifacts. Extensive experiments are validated by visual comparison, which demonstrates that the proposed algorithm outperforms existing warping-based methods.
Generalized Context Modeling With Multi-Directional Structuring and MDL-Based Model Selection for Heterogeneous Data Compression

This paper proposes generalized context modeling (GCM) for heterogeneous data compression. The proposed model extends the suffix of predicted subsequences in classic context modeling to arbitrary combinations of symbols in multiple directions. To address the selection of contexts, GCM constructs a model graph with a combinatorial structuring of finite order combination of predicted symbols as its nodes. The estimated probability for prediction is obtained by weighting over a class of context models that contain all the occurrences of nodes in the model graph. Moreover, separable context modeling in each direction is adopted for efficient prediction. To find optimal class of context models for prediction, the normalized maximum likelihood (NML) function is developed to estimate their structures and parameters, especially for heterogeneous data with large sizes. Furthermore, it is refined by context pruning to exclude the redundant models. Such model selection is optimal in the sense of minimum description length (MDL) principle, whose divergence is proven to be consistent with the actual distribution. It is shown that upper bounds of model redundancy for GCM are irrelevant to the size of data. GCM is validated in an extensive field of applications, e.g., Calgary corpus, executable files, and genomic data. Experimental results show that it outperforms most state-of-the-art context modeling algorithms reported.
Equilibrium modeling for 3D curvilinear structure tracking of confocal microscopy images

Neuron axon analysis through confocal microscopic image stack is dedicated in visualizing the geometrical features and topological characteristics of the 3D tubular biological objects, to ascertain the morphological properties and reconstruct the connectivity of neurons. This paper proposes a new curvilinear tracking algorithm which initializes a superellipsoid kernel into the tube by fitting the intensity energy distribution with multiple scales of steps and radii other than Hessian kernel. It is herein solved as an energy optimization in a graphical model with maximum likelihood, which preserves an equilibrium distribution across all the nodes with an attenuation penalty of orientation transition. Local potential energy diffusion of different axons is tracked by dynamic priority and pruning inference, to solve the cross-over problem. The centerline could be semi-automatically extracted following the selected initial point. Experimental results on 3D axon volumes verify that the proposed approach can handle complicated axon structures without elaborate segmentation.
Regional context model and dynamic Huffman binarization for adaptive entropy coding of multimedia

Classical context modeling and binarization algorithms on multimedia do not fully exploit their spatial correlations under the sequential assumption. This paper proposes a novel entropy coding scheme incorporating regional context modeling (RCM) and dynamic Huffman binarization (DHB) for multimedia. RCM evaluates the context order with the line distance in Cartesian coordinate system, so that the 2-D spatial correlations can be directly exploited. RCM predicts each symbol with the weighted estimation based on the line distance and appearing frequency of its context, which coincides with the principle for context-based prediction. On the other hand, DHB is proposed to adaptively assign the length of bins based on the estimated probability of symbols for coding. For optimal prediction and coding, frequent symbols are assigned with short bins to constrain the propagation of code redundancy. Applied into multimedia coding, extensive experimental results show that the proposed scheme achieves better coding performance than the benchmark coders.