nils-herrmann/clean_scirepeval huggingface dataset:
Image Categorization by Learning a Propagated Graphlet Path

Spatial pyramid matching is a standard architecture for categorical image retrieval. However, its performance is largely limited by the prespecified rectangular spatial regions when pooling local descriptors. In this paper, we propose to learn object-shaped and directional receptive fields for image categorization. In particular, different objects in an image are seamlessly constructed by superpixels, while the direction captures human gaze shifting path. By generating a number of superpixels in each image, we construct graphlets to describe different objects. They function as the object-shaped receptive fields for image comparison. Due to the huge number of graphlets in an image, a saliency-guided graphlet selection algorithm is proposed. A manifold embedding algorithm encodes graphlets with the semantics of training image tags. Then, we derive a manifold propagation to calculate the postembedding graphlets by leveraging visual saliency maps. The sequentially propagated graphlets constitute a path that mimics human gaze shifting. Finally, we use the learned graphlet path as receptive fields for local image descriptor pooling. The local descriptors from similar receptive fields of pairwise images more significantly contribute to the final image kernel. Thorough experiments demonstrate the advantage of our approach.
Nonrigid Structure-From-Motion From 2-D Images Using Markov Chain Monte Carlo

In this paper we present a new method for simultaneously determining 3-D shape and motion of a nonrigid object from uncalibrated 2-D images without assuming the distribution characteristics. A nonrigid motion can be treated as a combination of a rigid rotation and a nonrigid deformation. To seek accurate recovery of deformable structures, we estimate the probability distribution function of the corresponding features through random sampling, incorporating an established probabilistic model. The fitting between the observation and the projection of the estimated 3-D structure will be evaluated using a Markov chain Monte Carlo based expectation maximization algorithm. Applications of the proposed method to both synthetic and real image sequences are demonstrated with promising results.
Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection

Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods.
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression

Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors

Sparse coding methods have achieved great success in visual tracking, and we present a strong classifier and structural local sparse descriptors for robust visual tracking. Since the summary features considering the sparse codes are sensitive to occlusion and other interfering factors, we extract local sparse descriptors from a fraction of all patches by performing a pooling operation. The collection of local sparse descriptors is combined into a boosting-based strong classifier for robust visual tracking using a discriminative appearance model. Furthermore, a structural reconstruction error based weight computation method is proposed to adjust the classification score of each candidate for more precise tracking results. To handle appearance changes during tracking, we present an occlusion-aware template update scheme. Comprehensive experimental comparisons with the state-of-the-art algorithms demonstrated the better performance of the proposed method.
A maximum entropy feature descriptor for age invariant face recognition

In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition. First, a new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy. By densely sampling the encoded face image, sufficient discriminatory and expressive information can be extracted for further analysis. A new matching method is also developed, called identity factor analysis (IFA), to estimate the probability that two faces have the same underlying identity. The effectiveness of the framework is confirmed by extensive experimentation on two face aging datasets, MORPH (the largest public-domain face aging dataset) and FGNET. We also conduct experiments on the famous LFW dataset to demonstrate the excellent generalizability of our new approach.
Depth-Aware Image Seam Carving

Image seam carving algorithm should preserve important and salient objects as much as possible when changing the image size, while not removing the secondary objects in the scene. However, it is still difficult to determine the important and salient objects that avoid the distortion of these objects after resizing the input image. In this paper, we develop a novel depth-aware single image seam carving approach by taking advantage of the modern depth cameras such as the Kinect sensor, which captures the RGB color image and its corresponding depth map simultaneously. By considering both the depth information and the just noticeable difference (JND) model, we develop an efficient JND-based significant computation approach using the multiscale graph cut based energy optimization. Our method achieves the better seam carving performance by cutting the near objects less seams while removing distant objects more seams. To the best of our knowledge, our algorithm is the first work to use the true depth map captured by Kinect depth camera for single image seam carving. The experimental results demonstrate that the proposed approach produces better seam carving results than previous content-aware seam carving methods.
Message from Technical Chairs

It is our great pleasure to serve as Technical Program Chairs of IEEE ChinaSIP 2014, and welcome you to Xi'an, one of the most ancient and important cultural cities in China. Although it is still a baby as compared to other flagship conferences in our research community, ChinaSIP has received tremendous support from both the IEEE Signal Processing Society leadership as well as experts from all over the world. We would like to thank all volunteers for their tremendous behind-the-scene work that makes the conference a reality and all authors for their creative and compelling research — the conference would not exist without it.
The Recognition of the Point Symbols in the Scanned Topographic Maps

It is difficult to separate the point symbols from the scanned topographic maps accurately, which brings challenges for the recognition of the point symbols. In this paper, based on the framework of generalized Hough transform (GHT), we propose a new algorithm, which is named shear line segment GHT (SLS-GHT), to recognize the point symbols directly in the scanned topographic maps. SLS-GHT combines the line segment GHT (LS-GHT) and the shear transformation. On the one hand, LS-GHT is proposed to represent the features of the point symbols more completely. Its R-table has double level indices, the first one is the color information of the point symbols, and the other is the slope of the line segment connected a pair of the skeleton points. On the other hand, the shear transformation is introduced to increase the directional features of the point symbols; it can make up for the directional limitation of LS-GHT indirectly. In this way, the point symbols are detected in a series of the sheared maps by LS-GHT, and the final optimal coordinates of the setpoints are gotten from a series of the recognition results. SLS-GHT detects the point symbols directly in the scanned topographic maps, totally different from the traditional pattern of extraction before recognition. Moreover, several experiments demonstrate that the proposed method allows improved recognition in complex scenes than the existing methods.
Interactive Segmentation Using Constrained Laplacian Optimization

We present a novel interactive image segmentation approach with user scribbles using constrained Laplacian graph optimization. A novel energy framework is developed by adding the smoothing item in the cost function of Laplacian graph energy. To the best of our knowledge, our approach is the first to incorporate the normalized cuts and graph cuts algorithms into a unified energy optimization framework. The proposed approach is further accelerated by running the proposed optimization method on a band region when we segment the large images. Our acceleration strategy enables our approach to efficiently segment the large images, which yields about a 20-80 times speedup. The proposed approach is evaluated on both the publicly available data sets and our own data set with large images. The benefits of the proposed unified framework are also demonstrated both qualitatively and quantitatively. The experimental results show that our segmentation method achieves better performance of both boundary recall and error rate than the existing state-of-the-art approaches.
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.
Person Reidentification by Minimum Classification Error-Based KISS Metric Learning

In recent years, person reidentification has received growing attention with the increasing popularity of intelligent video surveillance. This is because person reidentification is critical for human tracking with multiple cameras. Recently, keep it simple and straightforward (KISS) metric learning has been regarded as a top level algorithm for person reidentification. The covariance matrices of KISS are estimated by maximum likelihood (ML) estimation. It is known that discriminative learning based on the minimum classification error (MCE) is more reliable than classical ML estimation with the increasing of the number of training samples. When considering a small sample size problem, direct MCE KISS does not work well, because of the estimate error of small eigenvalues. Therefore, we further introduce the smoothing technique to improve the estimates of the small eigenvalues of a covariance matrix. Our new scheme is termed the minimum classification error-KISS (MCE-KISS). We conduct thorough validation experiments on the VIPeR and ETHZ datasets, which demonstrate the robustness and effectiveness of MCE-KISS for person reidentification.
Learning Multilayer Channel Features for Pedestrian Detection

Pedestrian detection based on the combination of convolutional neural network (CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success. In general, HOG+LUV are used to generate the candidate proposals and then CNN classifies these proposals. Despite its success, there is still room for improvement. For example, CNN classifies these proposals by the fully connected layer features, while proposal scores and the features in the inner-layers of CNN are ignored. In this paper, we propose a unifying framework called multi-layer channel features (MCF) to overcome the drawback. It first integrates HOG+LUV with each layer of CNN into a multi-layer image channels. Based on the multi-layer image channels, a multi-stage cascade AdaBoost is then learned. The weak classifiers in each stage of the multi-stage cascade are learned from the image channels of corresponding layer. Experiments on Caltech data set, INRIA data set, ETH data set, TUD-Brussels data set, and KITTI data set are conducted. With more abundant features, an MCF achieves the state of the art on Caltech pedestrian data set (i.e., 10.40% miss rate). Using new and accurate annotations, an MCF achieves 7.98% miss rate. As many non-pedestrian detection windows can be quickly rejected by the first few stages, it accelerates detection speed by 1.43 times. By eliminating the highly overlapped detection windows with lower scores after the first stage, it is 4.07 times faster than negligible performance loss.
Integrating Conventional and Inverse Representation for Face Recognition

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.
Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection

An auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the Earth's magnetosphere. Detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. However, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. In this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (SCSLD) framework. Our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. To reduce noise interference inherent in current SLD methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. Experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed SCSLD method achieves good performance for motion analysis of aurora sequences. Moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice.
Lossless Data Embedding Using Generalized Statistical Quantity Histogram

Histogram-based lossless data embedding (LDE) has been recognized as an effective and efficient way for copyright protection of multimedia. Recently, a LDE method using the statistical quantity histogram has achieved good performance, which utilizes the similarity of the arithmetic average of difference histogram (AADH) to reduce the diversity of images and ensure the stable performance of LDE. However, this method is strongly dependent on some assumptions, which limits its applications in practice. In addition, the capacities of the images with the flat AADH, e.g., texture images, are a little bit low. For this purpose, we develop a novel framework for LDE by incorporating the merits from the generalized statistical quantity histogram (GSQH) and the histogram-based embedding. Algorithmically, we design the GSQH driven LDE framework carefully so that it: (1) utilizes the similarity and sparsity of GSQH to construct an efficient embedding carrier, leading to a general and stable framework; (2) is widely adaptable for different kinds of images, due to the usage of the divide-and-conquer strategy; (3) is scalable for different capacity requirements and avoids the capacity problems caused by the flat histogram distribution; (4) is conditionally robust against JPEG compression under a suitable scale factor; and (5) is secure for copyright protection because of the safe storage and transmission of side information. Thorough experiments over three kinds of images demonstrate the effectiveness of the proposed framework.
Superpixel fats for fast foreground extraction

Fast foreground extraction is an important and challenging problem. Although GrabCut can perform well in foreground extraction, the average accuracy is not satisfactory, and more importantly, its computational cost is large. In this paper, we propose a fast interactive foreground extraction based on superpixel GrabCut and matting. Specifically, we use a superpixel method to process images in order to improve the efficiency of the GrabCut. To obtain a refined mask, we employ the Shared matting method whose trimaps are implemented by our constructed fast and adaptive trimaps (FATs). Experimental results demonstrate the proposed method is more competitive and effective in the visual sense, E-Time and MSE criterions.
Robust Video Object Cosegmentation

With ever-increasing volumes of video data, automatic extraction of salient object regions became even more significant for visual analytic solutions. This surge has also opened up opportunities for taking advantage of collective cues encapsulated in multiple videos in a cooperative manner. However, it also brings up major challenges, such as handling of drastic appearance, motion pattern, and pose variations, of foreground objects as well as indiscriminate backgrounds. Here, we present a co segmentation framework to discover and segment out common object regions across multiple frames and multiple videos in a joint fashion. We incorporate three types of cues, i.e., intraframe saliency, interframe consistency, and across-video similarity into an energy optimization framework that does not make restrictive assumptions on foreground appearance and motion model, and does not require objects to be visible in all frames. We also introduce a spatio-temporal scale-invariant feature transform (SIFT) flow descriptor to integrate across-video correspondence from the conventional SIFT-flow into interframe motion flow from optical flow. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set. Experimental results show that our method outperforms the state-of-the-art on a new extensive data set (ViCoSeg).
Saliency Cut in Stereo Images

In this paper, we propose a novel saliency-aware stereo images segmentation approach using the high-order energy items, which utilizes the disparity map and statistical information of stereo images to enrich the high-order potentials. To the best of our knowledge, our approach is first one to formulate the automatic stereo cut as the high-order energy optimization problems, which simultaneously segments the foreground objects in left and right images using the proposed high-order energy function. The relationships of stereo correspondence by disparity maps are further employed to enhance the connections between the left and right images. Experimental results demonstrate that the proposed approach can effectively improve the saliency-aware segmentation performance of stereo images.