nils-herrmann/clean_scirepeval huggingface dataset:
CAE-L: An Ontology Modelling Cultural Behaviour in Adaptive Education

The presentation of learning materials in Adaptive Education Hypermedia is influenced by several factors such as learning style, background knowledge and cultural background, to name a few. In this paper, we introduce the notion of the CAE-L Ontology for modelling stereotype cultural artefacts in adaptive education. The Ontology design is based on the user study gathered from the respondents to the CAE questionnaire which determines the cultural artefacts that influence a learner¿s behaviour within an educational environment. We present a brief overview of the implementation and discuss the stereotype presentation styles from three different countries, namely China, Ireland and UK.
Climbing the semantic ladder: towards semantic semi-automatic image annotation using MPEG-7 descriptor schemas

The fast development of innovative tools to create user friendly and effective multimedia libraries, services and environments requires novel concepts to support storage, annotation and retrieval of huge amounts of digital audiovisual data. This article presents a technique to tackle the first instance of the problem in visual digital archives: classification using generic semantic descriptions. As a case study, classification abilities inherent to some important MFEG-7 low-level visual descriptors are explored and quantified.
Ontology-based forensic event detection using inference rules

Following the exponential deployment of surveillance systems across a wide-spread region of geographic locations, detection and representation of events has become a critical element in automated surveillance systems. In this paper, we focus on the high-level event recognition in the forensic domain. We present an extensive ontology framework for representing complex semantic events. To represent the richer relationships, which is not supported by the standard ontology semantics, event (activity) recognition is further augmented with rules. The proposed ontology builds on DOLCE ontology and relies on the linguistic and cognitive modelling of philosophical knowledge to achieve interoperability between proprietary surveillance systems. The expressiveness of the proposed ontology framework is described in the context of London Riots which took place in 2011.
Multi-class relevance feedback for collaborative image retrieval

In recent years, there is an emerging interest to analyse and exploit the log data recorded from different user interactions for minimising the semantic gap problem from multi-user collaborative environments. These systems are referred as “Collaborative Image Retrieval systems”. In this paper, we present an approach for collaborative image retrieval using multi-class relevance feedback. The relationship between users and concepts is derived using Lin Semantic similarity measure from WordNet. Subsequently, the Particle Swarm Optimisation classifier based relevance feedback is used to retrieve similar documents. The experimental results are presented on two well-known datasets namely Corel 700 and Flickr Image dataset. Similarly, the performance of the Particle Swarm Optimised retrieval engine is evaluated against the Genetic Algorithm optimised retrieval engine.
Optimizing Metrics Combining Low-Level Visual Descriptors for Image Annotation and Retrieval

An object oriented approach for key-word based image annotation and classification is presented. It considers combinations of low-level descriptors and suitable metrics to represent and measure similarity between semantically meaningful objects. The objective is to obtain "optimal" metrics based on a linear combination of single metrics and descriptors in a multi-feature space. The proposed approach estimates an optimal linear combination of predefined metrics by applying a multi-objective optimization technique based on a Pareto archived evolution strategy. The proposed approach has been evaluated and tested for annotation of objects in images
Histology Image Retrieval in Optimized Multifeature Spaces

Content-based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in histology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to automatically combine heterogeneous visual features for histology image retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images. The core of this approach is a multiobjective learning method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimization problem, and a multiobjective optimization strategy is employed in order to handle potential contradictions in the query images associated with the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content-based histology image retrieval by using an appropriately defined multifeature fusion model, which takes careful consideration of the structure and distribution of visual features.
Modeling arbitrary objects based on geometric surface conformity

We address the problem of efficient and flexible modeling of arbitrary three-dimensional (3-D) objects and the accurate tracking of the generated model. These goals are reached by combining available multiview image analysis tools with a straightforward 3-D modeling method, which exploit well-established techniques from both computer vision and computer graphics, improving and combining them with new strategies. The basic idea of the technique presented is to use feature points and relevant edges in the images as nodes and edges of an initial two-dimensional wire grid. The method is adaptive in the sense that an initial rough surface approximation is progressively refined at the locations where the triangular patches do not approximate the surface accurately. The approximation error is measured according to the distance of the model to the object surface, taking into account the reliability of the depth estimated from the stereo image analysis. Once the initial wireframe is available, it is deformed and updated from frame to frame according to the motion of the object points chosen to be nodes. At the end of this process we obtain a temporally consistent 3-D model, which accurately approximates the visible object surface and reflects the physical characteristics of the surface with as few planar patches as possible. The performance of the presented methods is confirmed by several computer experiments.
Exploiting social relationships for free-riders detection in minimum-delay P2P scalable video streaming

In this paper we describe a game theoretic framework for scalable video streaming over a peer-to-peer network that exploits social relationships. The proposed system integrates optimal resource allocation functionalities with an incentive provision mechanism for data sharing. First of all, we introduce an algorithm for packet scheduling that allows users to download a specific sub-set of the original scalable bit-stream, depending on the current network conditions. Furthermore, we present an algorithm that aims both at identifying free-riders and minimising transmission delay by exploiting social relationships among peers. Non social/uncooperative peers are cut out of this system, while users upload more data to those which have less to share, in order to fully exploit the resources of all the users.
H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to succeed the current standard, H.264/AVC, as the state of the art in video compression. However, there is a lot of legacy content encoded with H.264/AVC. This paper proposes and evaluates several transcoding algorithms from the H.264/AVC to the HEVC format. In particular, a novel transcoding architecture, in which the first frames of the sequence are used to compute the parameters so that the transcoder can learn the mapping for that particular sequence, is proposed. Then, two types of mode mapping algorithms are proposed. In the first solution, a single H.264/AVC coding parameter is used to determine the outgoing HEVC partitions using dynamic thresholding. The second solution uses linear discriminant functions to map the incoming H.264/AVC coding parameters to the outgoing HEVC partitions. This paper contains experiments designed to study the impact of the number of frames used for training in the transcoder. Comparisons with existing transcoding solutions reveal that the proposed work results in lower rate-distortion loss at a competitive complexity performance.
Adaptive packet scheduling for scalable video streaming with network coding

In advanced Peer-to-Peer delivery systems, each user downloads a video stream and at the same time uploads the same stream to other users. In live streaming, scalable video streaming is also proposed to allow partial decoding of the video streaming at a reduced resolution, frame-rate or quality, adapting to different display requirements and receptions conditions of heterogeneous receivers. In live streaming applications, Network Coding (NC) has the potential to bring substantially higher throughput while reducing transmission delays. However, the bandwidth efficiency of NC transmission is still a problem to live media streaming due to the lack of synchronization among peers. To solve this problem, we introduce a scalable streaming system, which includes a layer selection algorithm and a distributed packet scheduling algorithm. The proposed system is tested by streaming a scalable video over a peer-to-peer network. The experimental results confirm that the proposed algorithm achieves better video quality, better delivery ratio, and lower transmission redundancy.
Fuzzy color signatures

With the large and increasing amount of visual information available in digital libraries and the Web, efficient and robust systems for image retrieval are urgently needed. A compact color descriptor scheme and an efficient metric to compare and retrieve images is presented. An image adaptive color clustering method, called fuzzy color signature, is proposed. The original image colors are mapped into a small number of representative colors using a peaks detection function derived from the color distribution. Fuzzy color signatures are then used as image descriptors. To compare image descriptors the earth mover's distance is used. Several experiments have been conducted to assess the performance of the proposed technique.
On the impurity of street-scene video footage

In this paper, we present the technical challenges facing researchers in developing computer vision techniques to process street-scene videos from the wild. Video footage captured by surveillance CCTV cameras and hand-held devices such as mobile phones and body-mounted cameras worn by police officers pose particular difficulties. Video formats are varied and often non-Standards compliant which leads to apparent corruption when rendered using standard players. Footage is low-quality either in resolution or in sharpness caused by free movement of the camera, fast panning and zooming or weather conditions. We describe our experiences working with the Metropolitan Police in London to find a solution to these problems and enable computer vision techniques to be used in the forensic analysis of videos in criminal investigations.
An O-FDP Framework in 3D Model Based Reconstruction

Three dimensional scene synthesis and analysis has drawn considerable attention in manufacturing industries due to its multifaceted applications ranging from real time recognition, verification, vehicle guidance etc. Nowadays, there is a growing surge in FDP (Focused disparity map) of given objects of interest of multiview stereo images. In order to design customized specific application such as industrial part verification, we propose to use O-FDP (Optimal Focused Disparity Map) that spins off from the fact that only basic geometric information is available from DMU (Digital MockUp) models such as CATIA in industrial installations. Instead of using the whole image information, we use only the experiential information that is really necessary for application. The proposed framework unifies LIFE(Local Invariant Feature Extraction) techniques, edge information, epipolar geometry and object silhouette information. The framework results are presented and compared with state of the art work.
Gender-aided people recognition in photo collections

We show how to recognize people based on their faces in Consumer Photo Collections while also incorporating context in the form of gender information. We devise and explore a unified framework that has a graphical model along a distance-based face description method at its core. We jointly recognize people across an entire photo collection to also consider the specifics of photos that depict multiple people. Experiments on two datasets demonstrate and validate the effectiveness of our probabilistic approach compared to traditional methods that do not consider gender information.
MPEG-2 to HEVC Video Transcoding With Content-Based Modeling

This paper proposes an efficient MPEG-2 to High Efficiency Video Coding (HEVC) video transcoder. The objective of the transcoder is to migrate the abundant MPEG-2 video content to the emerging HEVC video coding standard. The transcoder introduces a content-based machine learning solution to predict the depth of the HEVC coding units. The proposed transcoder utilizes full re-encoding to find a mapping between the incoming MPEG-2 coding information and the outgoing HEVC depths of the coding units. Once the model is built, a switch to transcoding mode occurs. Hence, the model is content based and varies from one video sequence to another. The transcoder is compared against full re-encoding using the default HEVC fast motion estimation. Using HEVC test sequences, it is shown that a speedup factor of up to 3 is achieved, while reducing the bitrate of the incoming video by around 50%. In comparison to full re-encoding, an average of 3.9% excessive bitrate is encountered with an average PSNR drop of 0.1 dB. Since this is the first work to report on MPEG-2 to HEVC video transcoding, the reported results can be used as a benchmark for future transcoding research.
A multiresolution technique for video indexing and retrieval

This paper presents a novel approach to multiresolution analysis and scalability in video indexing and retrieval. A scalable algorithm for video parsing and key-frame extraction is introduced. The technique is based on real-time analysis of MPEG motion variables and scalable metrics simplification by discrete contour evolution. Furthermore, a hierarchical key-frame retrieval method using scalable colour histogram analysis is presented. It offers customisable levels of detail in the descriptor space, where the relevance order is determined by degradation of the image, and not by degradation of the image histogram. To assess the performance of the approach, several experiments have been conducted. Selected results are reported.
Residual error curvature estimation and adaptive classification for selective sub-pel precision motion estimation

We present a novel approach for adaptive precision motion estimation based on a classification of the residual error curvature. A fast algorithm is proposed to estimate the curvature of the interpolated residual surface using the error samples after integer precision motion estimation. We also propose an original technique to compute and successively update a set of thresholds using the information from previously coded frames. The optimal motion vector precision is then selected for each block according to the current thresholds. The approach is compared in terms of PSNR of the motion compensated reconstruction against conventional state of the art sub-pel motion estimation algorithms, and it is shown to efficiently reduce complexity and coding times of a typical video encoder with negligible effects on the prediction accuracy.
Performance Analysis of Text Halftone Modulation

This paper analyzes the use of text halftone modulation (THM) as a text hardcopy watermarking method. Using THM, text characters in a document have their luminances modified from the standard black to a gray level generated with a given halftone screen, according to a message to be transmitted. The application of THM has been discussed in ((R. Villan et al., 2006), (K. Matsuit and K. Tanaka, 1994)). In this paper, a spectral metric is proposed to detect the embedded message. Based on this metric, an error rate analysis of halftone modulation is presented considering the effects of the print and scan channel. Experiments validate the analysis and the applicability of the method.
A Comparative Study of Classification Techniques for Knowledge-Assisted Image Analysis

In this paper, four individual approaches to region classification for knowledge-assisted semantic image analysis are presented and comparatively evaluated. All of the examined approaches realize knowledge-assisted analysis via implicit knowledge acquisition, i.e. are based on machine learning techniques such as support vector machines (SVMs), self organizing maps (SOMs), genetic algorithm (GA)and particle swarm optimization (PSO). Under all examined approaches, each image is initially segmented and suitable low-level descriptors are extracted for every resulting segment. Then, each of the aforementioned classifiers is applied to associate every region with a predefined high-level semantic concept. An appropriate evaluation framework has been employed for the comparative evaluation of the above algorithms under varying experimental conditions.
Hierarchical Summarisation of Video Using Ant-Tree Strategy

Video summarisation approaches have various fields of application, specifically related to organising, browsing and accessing large video databases. In this paper, the appropriateness of biologically inspired models to tackle these problems is discussed and suitable strategy for unsupervised video summarisation is derived. In our proposal, we model the ability of ants to build live structures with their bodies in order to discover, in a distributed and unsupervised way, a tree-structured organization and summarisation of the video data. An experimental evaluation validating the feasibility and the robustness of this novel approach is presented.
K-Space Content Management and Retrieval System

In this contribution, the so-called "k-space content management and retrieval system " is presented. This multimodal framework is being developed within the K-Space project. K-Space is a network of leading European research teams from academia and industry conducting integrative research and dissemination activities in semantic inference for automatic and semi-automatic annotation and retrieval of multimedia content. To the large number of the system functionalities count: automatic shot detection, key-frame extraction, mpeg-7 feature extraction, multimedia content annotation, video streaming, visual search and retrieval, relevance feedback, and visual content classification. First, the system features from the user point of view are presented. Second, the theoretical description of algorithms leading to these functionalities follows.
Knowledge Space of Semantic Inference for Automatic Annotation and retrieval of Multimedia Content - K-Space

K-Space is a network of leading research teams from academia and industry conducting integrative research and dissemination activities in semantic inference for semi-automatic annotation and retrieval of multimedia content. K-Space exploits the complementary expertise of project partners, enables resource optimization and fosters innovative research in the field. The aim of K-Space research is to narrow the gap between lowlevel content descriptions that can be computed automatically by a machine and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media: The Semantic Gap. Specifically, K-Space integrative research focus on three areas: Content-based multimedia analysis; Knowledge extraction and Semantic multimedia.
ADORE: An Adaptive Holons Representation Framework for Human Pose Estimation

In this paper, the problem of human pose estimation in a 2D still image is addressed. A framework called adaptive holons representation (ADORE) that takes advantage of local and global cues is proposed to improve the pose estimation accuracy. In particular, ADORE is made up of two components: 1) the holons part, independent losses pose nets (ILPNs) is designed to first infer joints location on the global level; and 2) the adaptive part, convolutional local detectors (CLDs) is proposed to subsequently detect the joints in the potential regions generated by ILPN. Pose estimation is formulated as a classification problem toward body joints in ILPN, which consists of two independent loss layers that, respectively, instruct the learning of <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates of a joint. Experimental results on two challenging benchmark tasks demonstrate that our proposed framework is more efficient than other deep models and has desirable performance.
Optimized scalable video transmission over P2P network with hierarchical network coding

This paper proposes a new push-based peer-to-peer (P2P) communication method to optimally transmit scalable video packets over lossy networks. In our scheme, a rate allocation optimization is performed at the sender node. Different from previous sender/receiver driven schemes, our scheme does not need to update buffer maps or request packets periodically, which reduces the amount of redundant packets and yields to less traffic of video data over the Internet. The proposed optimized rate allocation algorithm calculates in advance the number of needed packets according to the cumulative uplink rate between senders and the receiver, and the loss rate of the link. After calculation, each sender sends the hierarchical network coded packets to receivers based on the estimated bandwidth and resource share allocation. With this method, the waste of bandwidth, and the delay caused by the communications among peers can both be reduced.
A flexible calibration method of multiple Kinects for 3D human reconstruction

In this paper, we present a simple yet effective calibration method for multiple Kinects, i.e. a method that finds the relative position of RGB-depth cameras, as opposed to conventional methods that find the relative position of RGB cameras. We first find the mapping function between the RGB camera and the depth camera mounted on one Kinect. With such a mapping function, we propose a scheme that is able to estimate the 3D coordinates of the extracted corners from a standard calibration chessboard. To this end, we are able to build the 3D correspondences between two Kinects directly. This simplifies the calibration to a simple Least-Square Minimization problem with very stable solution. Furthermore, by using two mirrored chessboard images on a thin board, we are able to calibrate two Kinects facing each other, something that is intractable using traditional calibration methods. We demonstrate our proposed method with real data and show very accurate calibration results, namely less than 7mm reconstruction error for objects at a distance of 1.5m, using around 7 frames for calibration.