nils-herrmann/clean_scirepeval huggingface dataset:
Real-time camera field-view tracking in soccer video

Soccer video content-based analysis remains a challenging problem due to the lack of structure in a soccer game. To automate game and tactic analysis, we need to detect and track important activities such as ball possession in a soccer video that is highly correlated to the camera's field-view. In this paper, we present a system that tracks the camera's field-view in a soccer video in real-time. It utilizes a host of content-based visual cues that are obtained by independent threads running in parallel. The result is visualized as an active rectangular bounding box that approximates the camera's field of view superimposed on a virtual soccer field. Experimental results show that the system can reliably track the camera field-view as the game progresses.
Cross-OSN User Modeling by Homogeneous Behavior Quantification and Local Social Regularization

In the context of social media services, data shortage has severally hindered accurate user modeling and practical personalized applications. This paper is motivated to leverage the user data distributed in disparate online social networks (OSN) to make up for the data shortage in user modeling, which we refer to as “cross-OSN user modeling.” Generally, the data that the same user distributes in different OSNs consist of both behavior data (i.e., interaction with multimedia items) and social data (i.e., interaction between users). This paper focuses on the following two challenges: 1) how to aggregate the users' cross-OSN interactions with multimedia items of the same modality, which we call cross-OSN homogeneous behaviors, and 2) how to integrate users' cross-OSN social data with behavior data. Our proposed solution to address the challenges consist of two corresponding components as follows. 1) Homogeneous behavior quantification, where homogeneous user behaviors are quantified based on their importance in reflecting user preferences. After quantification, the examined cross-OSN user behaviors are aggregated to construct a unified user-item interaction matrix. 2) Local social regularization, where the cross-OSN social data is integrated as regularization in matrix factorization-based user modeling at local topic level. The proposed cross-OSN user modeling solution is evaluated in the application of personalized video recommendation. Carefully designed experiments on self-collected Google+ and YouTube datasets have validated its effectiveness and the advantage over single-OSN-based methods.
Multi-task Correlation Particle Filter for Robust Object Tracking

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. The proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has several advantages. First, it can shepherd the sampled particles toward the modes of the target state distribution via the MCF, thereby resulting in robust tracking performance. Second, it can effectively handle large-scale variation via a particle sampling strategy. Third, it can effectively maintain multiple modes in the posterior density using fewer particles than conventional particle filters, thereby lowering the computational cost. Extensive experimental results on three benchmark datasets demonstrate that the proposed MCPF performs favorably against the state-of-the-art methods.
Web image retrieval via learning semantics of query image

The performance of traditional image retrieval approaches remains unsatisfactory, as they are restricted by the wellknown semantic gap and the diversity of textual semantics. To tackle these problems, we propose an improved image retrieval framework when querying with an image. The framework considers not only the discriminative power of various visual properties but also the semantic representation of the query image. Given a query image, we first perform CBIR to obtain some visually similar image sets corresponding to different visual properties separately. Then, a semantic representation to the query image is learnt from each image set. The semantic consistence among the textual indexes of each image set is measured in order to judge the confidence of various visual properties and the obtained semantic representation in search. Obtaining these items, both visually and semantically relevant images are returned to the user by a combined similarity measure. Experiments on a large-scale web images demonstrate the effectiveness and potential of the proposed framework.
Robust Visual Tracking via Exclusive Context Modeling

In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple group dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L1 tracker  is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.
Soccer replay detection using scene transition structure analysis

Replay scene detection is a useful technique for content based sports video analysis. Most current researchers try to find suitable visual and/or compressed domain features to detect the replay scene from a broadcast video. We present a novel approach using context information from the concurrence of replay and other types of shots to detect the replay scenes. We first perform a shot classification and then a scene transition structure analysis on the generated shot label sequence to extract the replay scene. The proposed model is computationally fast and some promising results were obtained.
Adaptive filtering of color noise using the Kalman filter algorithm

In the view of the drawbacks of the conventional Kalman filtering, a kind of adaptive Kalman filtering approach of color noise based on the correlative method of the system output is proposed. This approach builds the Kalman filtering model of color noise by adopting the equivalent measurement equation in order to avoid complicated computation and the expansion of the dimension of the filter. It is unnecessary to know the variance of the measurement noise beforehand so that it is closer to the actual situation. The correlative method of the system output is presented to estimate the variance of the measurement noise. The adaptive filtering of color noise using the Kalman filter algorithm is applied to solve the cephalometric images of stomatology. Several experimental results demonstrate the feasibility and good performance of the approach.
Singer identification based on vocal and instrumental models

We propose a novel method to identify the singer of a query song from the audio database. The database contains over 100 popular songs of solo singers. The rhythm structure of the song is analyzed using our proposed rhythm tracking method and the song is segmented into beat space time frames, where within the beat space time length the harmonic structure is quasi stationary. This inter-beat time resolution of the song is used for both feature extraction and training of the classifiers (i.e. support vector machine (SVM) for vocal/instrumental boundary detection and Gaussian mixture models (GMMs) for modeling the singer). Combining the instrumental music similarities in the songs of the same singer with the vocal model can improve the identification of the singer with an accuracy of over 87%.
Boosted Exemplar Learning for Action Recognition and Annotation

Human action recognition and annotation is an active research topic in computer vision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments.
Label localization by appearance guided graph inferring

Automatically localizing the image labels to the corresponding regions is a challenging but valuable task, which provides detailed semantic information for better image understanding and image retrieval. In this paper, we propose a novel appearance guided graph inferring (AGI) framework for label localization. The framework iterates with two stages: graph inferring and appearance learning. Given the image set, each image is over-segmented into a bag of small patches. In the first step, we adopt graph propagation based method to infer the patch labels collaboratively on the whole image set. A multi-cue graph is constructed for more consistent spatial layout and image label constraints are imposed in propagation. In the second step, SVM classifiers are trained as appearance models by gradually exploiting the inferring results. And then the patch labels are reevaluated by the learned appearance model and feedback to the first step. The global graph propagation and local appearance model complement each other by iteration. Extensive experiments on three public datasets, MSRC-v1, MSRC-v2 and SAIAPR TC-12, demonstrate the encouraging performance of our method in comparison with other baselines.
Adaptive Kalman filtering approach of color noise in cephalometric image

In the view of the drawbacks of the conventional Kalman filtering, a kind of adaptive Kalman filtering approach to color noise based on the correlative method of the system output is proposed to solve the cephalometric images of stomatology. This approach builds the Kalman filtering model of color noise by adopting the equivalent measurement equation in order to avoid complicated computation and the expansion of the dimension of the filter. It is also unnecessary to know the variance of the measurement noise beforehand so that it is closer to the actual situation. Results of several experiments are presented to demonstrate the feasibility and performance of this approach.
Friend transfer: Cold-start friend recommendation with cross-platform transfer learning of social knowledge

The emergence of various and disparate social media platforms has opened opportunities for the research on cross-platform media analysis. This provides huge potentials to solve many challenging problems which cannot be well explored in one single platform. In this paper, we investigate into cross-platform social relation and behavior information to address the cold-start friend recommendation problem. In particular, we conduct an in-depth data analysis to examine what information can better transfer from one platform to another and the result demonstrates a strong correlation for the bidirectional relation and common contact behavior between our test platforms. Inspired by the observations, we design a random walk-based method to employ and integrate these convinced social information to boost friend recommendation performance. To validate the effectiveness of our cross-platform social transfer learning, we have collected a cross-platform dataset including 3,000 users with recognized accounts in both Flickr and Twitter. We demonstrate the effectiveness of the proposed friend transfer methods by promising results.
Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering

Co-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called co-clustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.
P2T: Part-to-Target Tracking via Deep Regression Learning

Most existing part-based tracking methods are part-to-part trackers, which usually have two separated steps including the part matching and target localization. Different from existing methods, in this paper, we propose a novel part-to-target (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for P2T regression in an end-to-end framework via convolutional neural networks. The proposed model is designed not only to exploit the part context information to preserve object spatial layout structure, but also to learn part reliability to emphasize part importance for the robust P2T regression. We evaluate the proposed tracker on four challenging benchmark sequences, and extensive experimental results demonstrate that our method performs favorably against state-of-the-art trackers because of the powerful capacity of the proposed deep regression model.
Automatic Sports Video Genre Classification using Pseudo-2D-HMM

Building a generic content-based sports video analysis system remains a challenging problem because of the diversity in sports rules and game features which makes it difficult to discover generic low-level features or high-level modeling algorithms. One possible alternative is to first classify the sports genre and then apply specific sports domain knowledge to perform analysis. In this paper we describe a multi-level framework to automatically recognize the genre of the sports video. The system consists of a pseudo-2D-HMM classifier using low-level visual/audio features to evaluate the video clips. The experimental results are satisfactory and extension of the framework to a generic sports video analysis system is being implemented
A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video

Sports video annotation is important for sports video semantic analysis such as event detection and personalization. In this paper, we propose a novel approach for sports video semantic annotation and personalized retrieval. Different from the state of the art sports video analysis methods which heavily rely on audio/visual features, the proposed approach incorporates web-casting text into sports video analysis. Compared with previous approaches, the contributions of our approach include the following. 1) The event detection accuracy is significantly improved due to the incorporation of web-casting text analysis. 2) The proposed approach is able to detect exact event boundary and extract event semantics that are very difficult or impossible to be handled by previous approaches. 3) The proposed method is able to create personalized summary from both general and specific point of view related to particular game, event, player or team according to user's preference. We present the framework of our approach and details of text analysis, video analysis, text/video alignment, and personalized retrieval. The experimental results on event boundary detection in sports video are encouraging and comparable to the manually selected events. The evaluation on personalized retrieval is effective in helping meet users' expectations.
Automatic Visual Concept Learning for Social Event Understanding

Vision-based event analysis is extremely difficult due to the various concepts (object, action, and scene) contained in videos. Though visual concept-based event analysis has achieved significant progress, it has two disadvantages: visual concept is defined manually, and has only one corresponding classifier in traditional methods. To deal with these issues, we propose a novel automatic visual concept learning algorithm for social event understanding in videos. First, instead of defining visual concept manually, we propose an effective automatic concept mining algorithm with the help of Wikipedia, N-gram Web services, and Flickr. Then, based on the learned visual concept, we propose a novel boosting concept learning algorithm to iteratively learn multiple classifiers for each concept to enhance its representative discriminability. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for social event understanding.
Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification

With the rapidly increasing popularity of Social Media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and texts) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to find exactly the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multi-modal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification. Our BMM-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the supervised information of social events jointly. (2) It is suitable to large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset data to efficiently train the corresponding topic models. (3) It effectively exploits boosting document weight distribution by classification error, and can iteratively learn new topic model to correct the previously misclassified documents. We evaluate our BMM-SLDA on a real-world dataset and show extensive results, which show that our model outperforms state-of-the-art methods.
Robust Face-Name Graph Matching for Movie Character Identification

Automatic face identification of characters in movies has drawn significant research interests and led to many interesting applications. It is a challenging problem due to the huge variation in the appearance of each character. Although existing methods demonstrate promising results in clean environment, the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process. In this paper we present two schemes of global face-name matching based framework for robust character identification. The contributions of this work include the following. 1) A noise insensitive character relationship representation is incorporated. 2) We introduce an edit operation based graph matching algorithm. 3) Complex character changes are handled by simultaneously graph partition and graph matching. 4) Beyond existing character identification approaches, we further perform an in-depth sensitivity analysis by introducing two types of simulated noises. The proposed schemes demonstrate state-of-the-art performance on movie character identification in various genres of movies.
A self-learning system and its application in fault diagnosis

In this paper, a self-learning system based on objective used in fault diagnosis expert systems is presented It depends on the deep knowledge model of the diagnosed system and can improve diagnostic capability by expanding and satisfying the shallow knowledge base. Algorithm and principle of the self-learning system are described in details. As an application, the self-learning system has been embedded in a coal-cutter fauIt diagnosis expert system knowledge base and improve it. Generally, a learning process should satisfy conditions of (1) efficiency (the learning process should be quick enough to solve relevant problem), (2) accuracy (the learning process must have enough accurate solving means for a given problem) and (3) consistency (the learning result should be consistent with the original knowledge). In this paper, a self-learning system based on objective and its application in fault diagnosis expert system are presented.
Object-oriented synergetic expert system for fault diagnosis

In this paper, the structure and principle of a synergetic expert system for fault diagnosis (SESFD) are introduced in view of the limit of present expert system in application. SESFD is composed of four subordinate expert systems (SES) and a meta expert system (MES). MES controls and coordinates the work of SESs. Object-oriented programming has been used in system design. SESFD surmounts the brittleness of the single expert system and has a wide application prospective.<<ETX>>