nils-herrmann/clean_scirepeval huggingface dataset:
pVLAD: A discriminative image descriptor for image retrieval

For the past few years, the performance of object recognition and retrieval has been substantially boosted, which is largely attributed to the advent of many effective image descriptors. The most representative examples are the Fisher Vector (FV) and the Vector of Locally Aggregated Descriptors (VLAD). In this paper we focus on the latter. The original VLAD descriptor directly accumulates the sums of residual vectors as the final image representation, which, inevitably, ignores the varying discriminative power of the residual vectors. To address this problem, we propose to employ ℓp-norm IDF weighting on it, and obtain a novel image signature which we term pVLAD. The pVLAD descriptor puts more emphasis on the discriminative sub-blocks of VLAD to highlight their contributions to the similarity evaluation by calculating ℓp-norm IDF weights for respective sub-VLAD vectors. Meanwhile, ℓp-norm IDF weighting is helpful for alleviating burstiness phenomenon of visual codewords, and thereby provide superior performance. We evaluate our pVLAD descriptor on two publicly available benchmark databases, i.e. the INRIA Holidays and the University of Kentucky Benchmark (UKB). The experimental results demonstrate the significant advantages of pVLAD over the original VLAD formulation.
Horror video scene recognition via Multiple-Instance learning

Along with the ever-growing Web comes the proliferation of objectionable content, such as pornography, violence, horror information, etc. Horror videos, whose threat to childrens health is no less than pornographic video, are sometimes neglected by existing Web filtering tools. Consequently, an effective horror video filtering tool is necessary for preventing children from accessing these harmful horror videos. In this paper, by introducing color emotion and color harmony theories, we propose a horror video scenes recognition algorithm. Firstly, the video scenes are decomposed into a set of shots. Then we extract the visual features, audio features and emotional features of each shot, the video scene is viewed as a bag and each shot is treated as an instance of the corresponding bag. Finally, by combining the three features, the horror video scenes are recognized by the Multiple-Instance learning(MIL). According to the experimental results on diverse video scenes, the proposed scheme based on the emotional perception could effectively deal with the horror video scene recognition and promising results are achieved.
Multi-object Tracking under Occlusion Using Dual-Mode Graph Embedding

In this paper we address the problem of tracking multiple objects to know how objects are moving (e.g. occlusion relationships) while interacting with each other in a group, given models of their appearances that are learned online even when occlusion occurs. This aim is very different from the recently popular detection-based tracklets association approaches. In our approach, occlusion relationships between multiple objects are explicitly defined and deduction of the occlusion relationships is integrated into the whole tracking framework. Specifically, we deduce the joint state estimation problem in the multi-object tracking in a new decentralized strategy, that the single object tracking and the multi-object separating are viewed as one-versus-rest classification problems based on graph embedding framework. Two kinds of discriminative subspaces are learned: one for single object tracking which is robust to various appearance variations, the other for occlusion reasoning and decentralizing. Partial disappearance can also be addressed as an occlusion problem by this strategy. Experimental results demonstrate the effectiveness of our method.
Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.
Space-time neighborhood based hierarchical descriptor for action recognition

Recent work shows interest-point-based representation is greatly popular in action recognition, due to their simple implementation and good reliability. The neighborhood information of local descriptors usually improves the recognition accuracy. Taking inspiration from this observation, we propose a novel hierarchical neighborhood descriptor for action recognition. At low level, we propose the compound appearance and motion descriptor which describes the feature of neighboring interest points, rather than a single space-time interest point. At high level, another new neighborhood based descriptor is proposed to describe the spatial distribution of neighboring interest points. For classification, we apply multi-channel nonlinear SVM based on the hierarchical vocabulary. Experiments validate that our method achieves the state-of-the-art results on two benchmark datasets.
Distributed detection of network intrusions based on a parametric model

With the increasing requirements of fast response and privacy protection, how to detect network intrusions in a distributed architecture becomes a hot research area in the development of modern information security systems. However, it is a challenge to build such a system, given the difficulties brought by the mixed-attribute property of network connection data and the constraints on network communication. In this paper, we present a framework for distributed detection of network intrusions based on a parametric model. The parametric model can explicitly reflect the distributions of different intrusion types and handle the mixed-attribute data naturally. Based on the model, we can generate an accurate global intrusion detector with a very low cost of communication among the distributed detection sites, and no sharing of original network data is needed. Experimental results demonstrate the advantages of the proposed framework in the distributed intrusion detection application.
Combining sparse appearance features and dense motion features via random forest for action detection

This paper presents a new method to detect human actions in video by combining sparse appearance features and dense motion features in the unified random forest framework. We compute sparse appearance features to capture the main appearance changes and dense motion features to capture the tiny motion changes in the video. We take advantage of the randomization of channel selection in random trees to combine these two complementary types of features. In addition, linear classification is applied to grow each tree with high efficiency. Each leaf in these trees stores the class distribution and location information of the training samples and action detection for the test video is accomplished by Hough voting of the leaves in each tree. Experimental results demonstrate that our method achieves the state-of-the-art performance on two datasets.
Event Recognition Based on Top-Down Motion Attention

How to fuse static and dynamic information is a key issue in event analysis. In this paper, a top-down motion guided fusing method is proposed for recognizing events in an unconstrained news video. In the method, the static information is represented as a Bag-of-SIFT-features and motion information is employed to generate event specific attention map to direct the sampling of the interest points. We build class-specific motion histograms for each event so as to give more weight on the interest points that are discriminative to the corresponding event. Experimental results on TRECVID 2005 video corpus demonstrate that the proposed method can improve the mean average accuracy of recognition.
Using maximum consistency context for multiple target association in wide area traffic scenes

Tracking multiple vehicles in wide area traffic scenes is challenging due to high target density, severe similar target ambiguity, and low frame rate. In this paper, we propose a novel spatio-temporal context model, named maximum consistency context (MCC), to leverage the discriminative power and robustness in the scenario. For a candidate association, its MCC is defined as the most consistent association in its neighborhood. Such a maximum selection picks the reliable neighborhood context information while filtering out noisy distraction. We tested the proposed context modeling on multi-target tracking using three challenging wide area motion sequences. Both quantitative and qualitative results show clearly the effectiveness of MCC, in comparison with algorithms that use no context and standard spatial context respectively.
Evaluating Combinational Illumination Estimation Methods on Real-World Images

Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues.
Compact visual codebook for action recognition

Visual codebook has been popular in object classification as well as action analysis. However, its performance is often sensitive to the codebook size that is usually predefined. Moreover, the codebook generated by unsupervised methods, e.g., K-means, often suffers from the problem of ambiguity and weak efficiency. In other words, the visual codebook contains a lot of noisy and/or ambiguous words. In this paper, we propose a novel method to address these issues by constructing a compact but effective visual codebook using sparse reconstruction. Given a large codebook generated by K-means, we reformulate it in a sparse manner, and learn the weight of each word in the original visual codebook. Since the weights are sparse, they naturally introduce a new compact codebook. We apply this compact codebook to action recognition tasks and verify it on the widely used Weizmann action database. The experimental results show clearly the benefits of the proposed solution.
Region-based blood color detection and its application to bloody image filtering

Along with the widespread use of the World Wide Web, violent contents have affected our daily life. Although there are some investigations about violence video detection, few methods touch on the problem of violent and gory image detection. In this paper, we propose a region-based blood color detection algorithm. We first extract color and texture features from the detected bloody region of an image. We extract features of the whole image according to the global and local method. These features are fed into the SVM classifier. Experimental results have demonstrated the effectiveness of our proposed algorithm.
Contour tracking with abrupt motion

Traditional contour tracking methods can not handle abrupt motion or low frame rate video. This is because the basis of the traditional tracking lies in the assumption that the motion is smooth between consecutive frames. However, the abrupt motion destroys the foundation of this assumption. In this paper, we integrate the stochastic search into the level set evolution to reinstitute the continuity. Our approach can be viewed as a two-layer hierarchical level set-based tracking framework in which Particle Swarm Optimization (PSO) and level set evolution are fused seamlessly. In the first layer, the PSO is adopted to capture the global motion of the object. The coarse contour is obtained by applying the global motion to the contour in the previous frame. For the second layer, the level set evolution based on the coarse contour is carried out to track the local deformation, which results in the actual contour. The promising experimental results for numerous real videos reveal the effectiveness of our approach.
Image Classification Using Multiscale Information Fusion Based on Saliency Driven Nonlinear Diffusion Filtering

In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates.
Boosted cannabis image recognition

With the large number of Web sites promoting the use of illicit drugs, it has become important to screen these sites for the protection of children on the Internet. Conventional keyword-based approaches are not sufficient because these Web sites often have lots of images and little meaningful words than prices. We propose an AdaBoost-based algorithm for cannabis image recognition. This is the first known attempt at computerized detection of illicit drug Web contents using images. The main technical contributions of our work are two-fold. First, we introduce a novel weak classifier which considers the inherently structural property or ldquoself-similarityrdquo of the cannabis plants. The self-correlation structural characteristics of cannabis can be used as a discriminative property for the purpose of cannabis image recognition. Second, we propose a rapid weak classifier finder, which can efficiently select discriminative weak classifiers from the weak classifier space with little degradation to the classification accuracy. Experiments on real world images have demonstrated improved performance of our method over other methods.
Heat Kernel Based Local Binary Pattern for Face Representation

Face classification has recently become a very hot research topic in computer vision and multimedia information processing. It has many potential applications, in which face representation is the most fundamental task. Most existing face representation methods perform poorly in capturing the intrinsic structural information of face appearance. To address this problem, we propose a novel multiscale heat kernel based face representation, for heat kernels perform well in characterizing the topological structural information of face appearance. Further, the local binary pattern (LBP) descriptor is incorporated into the multiscale heat kernel face representation for the purpose of capturing texture information of face appearance. As a result, we have the heat kernel based local binary pattern (HKLBP) descriptor. Finally, a Support Vector Machine (SVM) classifier is learned in the HKLBP feature space for face classification. Experimental results demonstrate the effectiveness and superiority of our face classification framework.
Group action recognition in soccer videos

Group action recognition in soccer videos is a challenging problem due to the difficulties of group action representation and camera motion estimation. This paper presents a novel approach for recognizing group action with a moving camera. In our approach, ego-motion is estimated by the Kanade-Lucas-Tomasi feature sets on successive frames. The optical flow is then computed on compensated frames. Due to the inaccurate ego-motion estimation, the optical flow can not reflect accurate motion of objects. In this paper, we propose a new motion descriptor which treats the optical flow as spatial patterns and extracts accurate global motion from the noisy optical flow. The latent-dynamic conditional random field model is employed to recognize group action. Experimental results show that our approach is promising.
Topic Detection and Tracking for Threaded Discussion Communities

The threaded discussion communities are one of the most common forms of online communities, which are becoming more and more popular among web users. Everyday a huge amount of new discussions are added to these communities, which are difficult to summarize and search. In this paper, we propose a topic detection and tracking (TDT) method for the discussion threads. Most existing TDT methods deal with the news stories, but the language used in discussion data are much more casual, oral and informal compared with news data. To solve this problem, we design several extensions to the basic TDT framework, focusing on the very nature of discussion data, including a thread/post activity validation step, a term pos-weighting strategy, and a two-level decision framework considering not only the content similarity but also the user activity information. Experiment results show that our pro-posed method greatly improves current TDT methods in real discussion community environment. The discussion data can be better organized for searching and visualization with the help of TDT.
Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.
The Visual Object Tracking VOT2013 Challenge Results

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).
Rank Aggregation Based Text Feature Selection

Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods proposed in previous work leading to the “selection trouble” that how to select an appropriate filtering method for a given text data set. Since to find the best filtering method is usually intractable in real application, this paper takes an alternative path. We propose a feature selection framework that fuses the results obtained by different filtering methods. In fact, deriving a better rank list from different rank lists, known as rank aggregation, is a hot topic studied in many disciplines. Based on the proposed framework and Markov chains rank aggregation techniques, in this paper, we present two new feature selection methods: FR-MC1 and FR-MC4. We also introduce a perturbation algorithm to alleviate the drawbacks of Markov chains rank aggregation techniques. Empirical evaluation on two public text data sets shows that the two new feature selection methods achieve better or comparable results than classical filtering methods, which also demonstrate the effectiveness of our framework.