nils-herrmann/clean_scirepeval huggingface dataset:
An adaptive multimedia transmission protocol for distributed multimedia applications

In this paper, an end-to-end real-time adaptive protocol for multimedia transmission is presented. The transmission rate is determined by the quadratic probing algorithm that can obtain the maximal utilization of the client buffer and minimal allocation of the network bandwidth. It is also coupled with a congestion control mechanism that can effectively decrease the packet loss rate during network congestion. We investigate the performance of our quadratic probing algorithm in different congestion levels under both Local Area Net (LAN) and Internet environment. Performance analysis reveals that our approach is more robust in avoiding overflow and underflow in different network congestion levels, and adapting to the changing network delays. Comparisons are made with the fixed rate approach and the rate by playback requirement approach. Experimental results show that the proposed real time protocol with rate adjusting quadratic probing algorithm is efficient in utilizing the network resources and decreasing the packet loss ratios.
Weighted Subspace Filtering and Ranking Algorithms for Video Concept Retrieval

The rapid increase in the amount of digital data has posed significant challenges to retrieval systems, which are expected to effectively and efficiently filter the data and provide relevant results. Filtering is a technique that automatically selects features or data instances to represent the data, reduce storage costs, prune redundancy, decrease computation costs, enhance model learning performance, and dynamically deliver the media. On the other hand, ranking is a critical step to render as many relevant results as possible on the basis of some loss functions or similarity measures. This article presents a novel retrieval framework that consists of weighted subspace-based filtering and ranking components to address these challenges. One critical issue in multimedia retrieval is the difficulty of comparing semantics between low-level features (such as color and shape) and high-level concepts (such as sky and ocean). Because multiple correspondence analysis (MCA) is powerful in exploring relationships among high-dimensional categorical variables, it is attractive to apply MCA to explore relationships between feature categories and concept classes. However, existing correlation based algorithms are weak in capturing semantics through a numeric measure such as conditional entropy to guide the search of features. Most existing subspace-based algorithms, while considering semantics, mainly focus on the representations of features in new spaces or relationships among high-level concepts. Therefore, our filtering algorithm transfers the semantic correlation captured in the subspace to the feature weights to distinguish features and imbalanced data instances. Relevance of retrieval results is also crucial in multimedia, where effectiveness is measured by the ranking process. Our proposed algorithm is designed to overcome the limitation of the most commonly used ranking methods, which treat all data instances equally within one group during the training step. Our algorithm ranks data instances using dissimilarity values in the subspace toward both positive and nega tive one-class models from the learning phase. The idea behind this is that even those data instances in the same relevant group may have implicit different significances. For exam ple, the data instances closer to the relevant group's center could indicate more relevance than those that are further from the center. Meanwhile, a low-dimensional representation of the data instances could compactly characterize the structure of the data instance groups, making it possible to achieve an easy separation of relevant and irrelevant groups and effectively rank the retrieved results. To demonstrate the effectiveness of the proposed framework, we evaluated 30 high-level concepts and data sets from Trecvid 2008 and 2009.
A progressive morphological filter for removing nonground measurements from airborne LIDAR data

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.
Hierarchical Temporal Association Mining for Video Event Detection in Video Databases

With the proliferation of multimedia data and ever growing requests for multimedia applications, new challenges emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.
Spatial-temporal motion information integration for action detection and recognition in non-static background

Various motion detection methods have been proposed in the past decade, but there are seldom attempts to investigate the advantages and disadvantages of different detection mechanisms so that they can complement each other to achieve a better performance. Toward such a demand, this paper proposes a human action detection and recognition framework to bridge the semantic gap between low-level pixel intensity change and the high-level understanding of the meaning of an action. To achieve a robust estimation of the region of action with the complexities of an uncontrolled background, we propose the combination of the optical flow field and Harris3D corner detector to obtain a new spatial-temporal estimation in the video sequences. The action detection method, considering the integrated motion information, works well with the dynamic background and camera motion, and demonstrates the advantage of the proposed method of integrating multiple spatial-temporal cues. Then the local features (SIFT and STIP) extracted from the estimated region of action are used to learn the Universal Background Model (UBM) for the action recognition task. The experimental results on KTH and UCF YouTube Action (UCF11) data sets show that the proposed action detection and recognition framework can not only better estimate the region of action but also achieve better recognition accuracy comparing with the peer work.
Stochastic clustering for organizing distributed information sources

The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.
Incorporating real-valued multiple instance learning into relevance feedback for image retrieval

This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.
Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems

One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.
Multi-model integration for long-term time series prediction

Long-term (multi-step-ahead) time series prediction is a much more challenging task comparing to the short-term (one-step-ahead) time series prediction. This is due to the increasing uncertainty and the lack of knowledge about the future trend. In this paper, we propose a multi-model integration strategy to 1) generate predicted values using multiple predictive models; and then 2) integrate the predicted values to generate a final predicted value as the output. In the first step, a k-nearest-neighbor (k-NN) based least squares support vector machine (LS-SVM) approach is used for long-term time series prediction. An autoregressive model is then employed in the second step to combine the predicted values from the multiple k-NN based LS-SVM models. The proposed multi-model integration strategy is evaluated using six datasets, and the experimental results demonstrate that the proposed strategy consistently outperforms some existing predictors.
Utilization of Co-occurrence Relationships between Semantic Concepts in Re-ranking for Information Retrieval

Semantic information retrieval is a popular research topic in the multimedia area. The goal of the retrieval is to provide the end users with as relevant results as possible. Many research efforts have been done to build ranking models for different semantic concepts (or classes). While some of them have been proven to be effective, others are still far from satisfactory. Our observation that certain target semantic concepts have high co-occurrence relationships with those easy-to-retrieve semantic concepts (or called reference semantics) has motivated us to utilize such co-occurrence relationships between semantic concepts in information retrieval and re-ranking. In this paper, we propose a novel semantic retrieval and re-ranking framework that takes advantage of the co-occurrence relationships between a target semantic concept and a reference semantic concept to re-rank the retrieved results. The proposed framework discretizes the training data into a set of feature-value pairs and employs Multiple Correspondence Analysis (MCA) to capture the correlation in terms of the impact weight between feature-value pairs and the positive-positive class in which the data instances belong to both the target semantic concept and the reference semantic concept. A combination of all these impact weights is utilized to re-rank the retrieved results for the target semantic concept. Comparative experiments are designed and evaluated on TRECVID 2005 and TRECVID 2010 video collections with public-available ranking scores. Experimental results on different retrieval scales demonstrate that our proposed framework can enhance the retrieval results for the target semantic concepts in terms of average precision, and the improvements for some semantic concepts are promising.
Mining user access behavior on the WWW

In this paper, an affinity-based approach that provides good similarity measures for Web document clustering to discover user access behavior on the World Wide Web (WWW) is proposed. The proposed approach generates the similarity measures for groups of Web documents by considering the user access patterns. Any clustering algorithm using better similarity measures should yield better clusters for discovering user access behavior. By utilizing the discovered user access behavior, for example, the companies can precisely target their potential customers and convince them to purchase their products or services in electronic commerce. An experiment on a real data set is conducted and the experimental result shows that the proposed approach yields a better performance than the cosine coefficient and the Euclidean distance method under the partitioning around medoid (PAM) method.
A Multi-Buffer Scheduling Scheme for Video Streaming

In this paper, we propose a multi-buffer scheduling scheme for streaming video systems. A transmission rate is obtained via a rate control algorithm, which optimally utilizes the network bandwidth and client buffer resources. The server side maintains multiple buffers for packets of different importance levels. It schedules the transmission of each packet based on the source buffer size and playback deadline to reduce the end-to-end distortion. The performance of proposed scheme is evaluated in terms of peak signal-to-noise ratio (PSNR) in the simulations, and the simulation results demonstrate the improvement of the average PSNR values compared with two other scheduling schemes
k-NN based LS-SVM framework for long-term time series prediction

Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function, which integrates the Euclidean distance and the dissimilarity of the trend of a time series, is defined for the k-NN approach. By selecting similar instances (i.e., nearest neighbors) in the training dataset for each testing instance based on the k-NN approach, the complexity of training an LS-SVM regressor is reduced significantly. Experiments on two types of datasets were conducted to compare the prediction performance of the proposed framework with the traditional LS-SVM approach and the LL-MIMO (Multi-Input Multi-Output Local Learning) approach at the prediction horizon 20. The experimental results demonstrate that the proposed framework outperforms both traditional LS-SVM approach and LL-MIMO approach in prediction. Furthermore, experimental results also show the promising long-term prediction ability of the proposed framework even when the prediction horizon is large (up to 180).
Affinity-based similarity measure for Web document clustering

Compared to the regular documents, the major distinguishing characteristics of the Web documents are the dynamic hyper-structure. Thus, in addition to terms or keywords for regular document clustering, Web document clustering can incorporate some dynamic information such as the hyperlinks and the access patterns extracted from the user query logs. In this paper, we extend the concept of document clustering into Web document clustering by introducing the strategy of affinity-based similarity measure, which utilizes the user access patterns in determining the similarities among Web documents via a probabilistic model. Several comparison experiments are conducted using a real data set and the experimental results demonstrate that the proposed similarity measure outperforms the cosine coefficient and the Euclidean distance method under different document clustering algorithms.
LDA-Based Retrieval Framework for Semantic News Video Retrieval

Topic-based language model has attracted much attention as the propounding of semantic retrieval in recent years. Especially for the ASR text with errors, the topic representation is more reasonable than the exact term representation. Among these models, Latent Dirichlet Allocation(LDA) has been noted for its ability to discover the latent topic structure, and is broadly applied in many text-related tasks. But up to now its application in information retrieval(IR) is still limited to be a supplement to the standard document models, and furthermore, it has been pointed out that directly employing the basic LDA model will hurt retrieval performance. In this paper, we propose a lexicon-guided two-level LDA retrieval framework. It uses the HowNet to guide the first-level LDA model's parameter estimation, and further construct the second-level LDA models based on the first-level's inference results. We use TRECID 2005 ASR collection to evaluate it, and compare it with the vector space model(VSM) and latent semantic Indexing(LSI). Our experiments show the proposed method is very competitive.
An end-to-end video transmission framework with efficient bandwidth utilization

We present a new framework for streaming video over Internet in this paper. The sending rate is dynamically adjusted to obtain a maximal utilization of the client buffer and minimal bandwidth allocation. To guarantee a more reliable and better quality of delivery of the video frames, retransmission and selective drop of different frame packets are integrated into our framework. Under severe network congestion, an adaptive playback schedule can provide relative high video quality at a low frame rate. Comparisons are made with the most current streaming approaches using the H.26L video coder to evaluate the performance of the framework. Simulations results show that PSNR is increased in our approach, which provides a better quality of the decoded frames, and the quality of the decoded frames also changes more smoothly
Mining High-Level Features from Video Using Associations and Correlations

Association rule mining (ARM) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high efficiency and accuracy. Two important processes in mining the association rules for classification are rule generation and rule selection. In this paper, a novel high-level feature detection framework using the ARM technique together with the correlations among the feature-value pairs is proposed. A new association rule mining (ARM) algorithm has been developed, where the N-feature-value pairs are generated using a combined measure based on (1) the existence of the (N-1)-feature-value pairs (where N is larger than 1), (2) the correlation between different N-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intra-similarity. The final association classification rules are selected by using the calculated harmonic mean of the similarity values. The proposed framework enables the automatic discovery and generation of the N-feature-value pair association rules from the $1$-feature-value pairs for classification. Experimenting with 15 high-level features (concepts) and benchmark data sets from TRECVID, our proposed framework achieves promising performance and outperforms three other well-known classifiers (Decision Trees, Support Vector Machine, and Neural Networks) which are commonly used for performance comparison in the TRECVID community.
Colour Image Filtering with Component-Graphs

Mathematical morphology, initially devoted to binary and grey-level image processing, also offers opportunities to develop efficient tools for multivalued - and in particular, colour - images. In this context, connected operators are increasingly considered as a relevant way to obtain such tools, mainly for image filtering and segmentation purposes. In this article, we focus on connected operators based on component-trees and their extension to multivalued images, namely component-graphs. Beyond the classical colour-handling strategies, we show how component-graphs can be algorithmically used to efficiently handle the whole structural information gathered by colour spaces, in order to finally design original image filtering tools.