nils-herrmann/clean_scirepeval huggingface dataset:
A Probabilistic Associative Model for Segmenting Weakly Supervised Images

Weakly supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this paper). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the sky or the sea). To compare different-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network to capture the semantic associations between postembedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization.
Human Gait Recognition With Matrix Representation

Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database
Learning a Nonnegative Sparse Graph for Linear Regression

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.
Image retrieval with EMD for new perceptual color feature

This paper presents a novel scheme for perceptual Content-based Image Retrieval. Semantic information can be protected during the retrieval by means of new adaptive color quantization algorithm. The entire method is consisted of two steps: perceptual color feature development and comparing quantized color vectors. The experimental results show that the proposed scheme outperforms the traditional methods in terms of semantic retrieval and computing complexity. The groundtruth are made based on 60,000 images from Corel database.
Sparse Learning with Stochastic Composite Optimization

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/\lambda T)$</tex-math><alternatives> <inline-graphic xlink:href="zhang-ieq1-2578323.gif"/></alternatives></inline-formula>, but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\sqrt{\log (1/\delta)/T})$ </tex-math><alternatives><inline-graphic xlink:href="zhang-ieq2-2578323.gif"/></alternatives></inline-formula> with <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math><alternatives> <inline-graphic xlink:href="zhang-ieq3-2578323.gif"/></alternatives></inline-formula> is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(\log (\log (T)/\delta)/\lambda T)$</tex-math><alternatives> <inline-graphic xlink:href="zhang-ieq4-2578323.gif"/></alternatives></inline-formula>.
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
Auto-weighted two-dimensional principal component analysis with robust outliers

Two-dimensional principal component analysis (2DPCA) serves as an efficient approach for both dimensionality reduction and high-quality reconstruction. However, conventional 2DPCA method is sensitive to the outliers such that associated results could be compromised. To strengthen the robustness of conventional 2DPCA method, we try to propose a novel robust two-dimensional principal component analysis with optimal mean (R2DPCA-OM) method to automatically achieve the optimal mean. Besides, the experimental results illustrate that the proposed R2DPCA-OM method could obtain the optimal subspaces and mean, such that dimensionality is reduced with less reconstruction error. Consequently, superiority and effectiveness of the proposed R2DPCA-OM method could be verified analytically and empirically.
Matrix completion by Truncated Nuclear Norm Regularization

Estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. Most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. However, by minimizing the nuclear norm, all the singular values are simultaneously minimized, and thus the rank can not be well approximated in practice. In this paper, we propose a novel matrix completion algorithm based on the Truncated Nuclear Norm Regularization (TNNR) by only minimizing the smallest N-r singular values, where N is the number of singular values and r is the rank of the matrix. In this way, the rank of the matrix can be better approximated than the nuclear norm. We further develop an efficient iterative procedure to solve the optimization problem by using the alternating direction method of multipliers and the accelerated proximal gradient line search method. Experimental results in a wide range of applications demonstrate the effectiveness of our proposed approach.
Efficient Search and Localization of Human Actions in Video Databases

As digital video databases grow, so grows the problem of effectively navigating through them. In this paper we propose a novel content-based video retrieval approach to searching such video databases, specifically those involving human actions, incorporating spatio-temporal localization. We outline a novel, highly efficient localization model that first performs temporal localization based on histograms of evenly spaced time-slices, then spatial localization based on histograms of a 2-D spatial grid. We further argue that our retrieval model, based on the aforementioned localization, followed by relevance ranking, results in a highly discriminative system, while remaining an order of magnitude faster than the current state-of-the-art method. We also show how relevance feedback can be applied to our localization and ranking algorithms. As a result, the presented system is more directly applicable to real-world problems than any prior content-based video retrieval system.
Learning Similarity With Multikernel Method

In the field of machine learning, it is a key issue to learn and represent similarity. This paper focuses on the problem of learning similarity with a multikernel method. Motivated by geometric intuition and computability, similarity between patterns is proposed to be measured by their included angle in a kernel-induced Hilbert space. Having noticed that the cosine of such an included angle can be represented by a normalized kernel, it can be said that the task of learning similarity is equivalent to learning an appropriate normalized kernel. In addition, an error bound is also established for learning similarity with the multikernel method. Based on this bound, a boosting-style algorithm is developed. The preliminary experiments validate the effectiveness of the algorithm for learning similarity.
Weakly Supervised Human Fixations Prediction

Automatically predicting human eye fixations is a useful technique that can facilitate many multimedia applications, e.g., image retrieval, action recognition, and photo retargeting. Conventional approaches are frustrated by two drawbacks. First, psychophysical experiments show that an object-level interpretation of scenes influences eye movements significantly. Most of the existing saliency models rely on object detectors, and therefore, only a few prespecified categories can be discovered. Second, the relative displacement of objects influences their saliency remarkably, but current models cannot describe them explicitly. To solve these problems, this paper proposes weakly supervised fixations prediction, which leverages image labels to improve accuracy of human fixations prediction. The proposed model hierarchically discovers objects as well as their spatial configurations. Starting from the raw image pixels, we sample superpixels in an image, thereby seamless object descriptors termed object-level graphlets (oGLs) are generated by random walking on the superpixel mosaic. Then, a manifold embedding algorithm is proposed to encode image labels into oGLs, and the response map of each prespecified object is computed accordingly. On the basis of the object-level response map, we propose spatial-level graphlets (sGLs) to model the relative positions among objects. Afterward, eye tracking data is employed to integrate these sGLs for predicting human eye fixations. Thorough experiment results demonstrate the advantage of the proposed method over the state-of-the-art.
Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook

Fast and accurately categorizing the millions of aerial images on Google Maps is a useful technique in pattern recognition. Existing methods cannot handle this task successfully due to two reasons: 1) the aerial images' topologies are the key feature to distinguish their categories, but they cannot be effectively encoded by a conventional visual codebook and 2) it is challenging to build a realtime image categorization system, as some geo-aware Apps update over 20 aerial images per second. To solve these problems, we propose an efficient aerial image categorization algorithm. It focuses on learning a discriminative topological codebook of aerial images under a multitask learning framework. The pipeline can be summarized as follows. We first construct a region adjacency graph (RAG) that describes the topology of each aerial image. Naturally, aerial image categorization can be formulated as RAG-to-RAG matching. According to graph theory, RAG-to-RAG matching is conducted by enumeratively comparing all their respective graphlets (i.e., small subgraphs). To alleviate the high time consumption, we propose to learn a codebook containing topologies jointly discriminative to multiple categories. The learned topological codebook guides the extraction of the discriminative graphlets. Finally, these graphlets are integrated into an AdaBoost model for predicting aerial image categories. Experimental results show that our approach is competitive to several existing recognition models. Furthermore, over 24 aerial images are processed per second, demonstrating that our approach is ready for real-world applications.
Reduced-Reference IQA in Contourlet Domain

The human visual system (HVS) provides a suitable cue for image quality assessment (IQA). In this paper, we develop a novel reduced reference (RR) IQA scheme by incorporating the merits from the contourlet transform, contrast sensitivity function (CSF), and Weber's law of just noticeable difference (JND). In this scheme, the contourlet transform is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. CSF is applied to weight coefficients obtained by the contourlet transform to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the laboratory for image and video engineering database against the subjective mean opinion score and demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images.
SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression

Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high- and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high- and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. The linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. To effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. The cascaded learning process gradually decreases the gap of high-frequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods.
Survey of Spatio-Temporal Interest Point Detection Algorithms in Video

Recently, increasing attention has been paid to the detection of spatio-temporal interest points (STIPs), which has become a key technique and research focus in the field of computer vision. Its applications include human action recognition, video surveillance, video summarization, and content-based video retrieval. Amount of work has been done by many researchers in STIP detection. This paper presents a comprehensive review on STIP detection algorithms. We first propose the detailed introductions and analysis of the existing STIP detection algorithms. STIP detection algorithms are robust in detecting interest points for video in the spatio-temporal domain. Next, we summarize the existing challenges in the STIP detection for video, such as low time efficiency, poor robustness with respect to camera movement, illumination change, perspective occlusion, and background clutter. This paper also presents the application situations of STIP and discusses the potential development trends of STIP detection.
Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs.

Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented.
Universal no reference image quality assessment metrics based on local dependency

No reference image quality assessment (NR-IQA) is to evaluate image quality blindly without the ground truth. Most of the emerging NR-IQA algorithms are only effective for some specific distortion. Universal metrics that can work for various categories of distortions have hardly been explored, and the algorithms available are not fully adequate in performance. In this paper, we study the local dependency (LD) characteristic of natural images, and propose two universal NR-IQA metrics: LD global scheme (LD-GS) and LD two-step scheme (LD-TS). We claim that the local dependency characteristic among wavelet coefficients is disturbed by various distortion processes, and the disturbances are strongly correlated to image qualities. Experimental results on LIVE database II demonstrate that both the proposed metrics are highly consistent with the human perception and outpace the state-of-the-art NR-IQA indexes and some full reference quality indicators for diverse distortions and across the entire database.
Identifying Objective and Subjective Words via Topic Modeling

It is observed that distinct words in a given document have either strong or weak ability in delivering facts (i.e., the <italic>objective</italic> sense) or expressing opinions (i.e., the <italic>subjective</italic> sense) depending on the topics they associate with. Motivated by the intuitive assumption that different words have varying degree of <italic>discriminative</italic> power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as <inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>dentified <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>bjective–<inline-formula> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula>ubjective latent Dirichlet allocation (LDA) (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>osLDA) is proposed in this paper. In the <inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>osLDA model, the simple Pólya urn model adopted in traditional topic models is modified by incorporating it with a probabilistic generative process, in which the novel “<italic>Bag-of-Discriminative-Words</italic>” (BoDW) representation for the documents is obtained; each document has two different BoDW representations with regard to objective and subjective senses, respectively, which are employed in the joint objective and subjective classification instead of the traditional Bag-of-Topics representation. The experiments reported on documents and images demonstrate that: 1) the BoDW representation is more predictive than the traditional ones; 2) <inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>osLDA boosts the performance of topic modeling via the joint discovery of latent topics and the different objective and subjective power hidden in every word; and 3) <inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>osLDA has lower computational complexity than supervised LDA, especially under an increasing number of topics.