nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Photo Album Based on MPEG-4 Compatible Application Format

In this paper, we propose a promising photo album system that enables augmented use of digital photos over a wide range of multimedia devices and semantic consumption of photos as well. The proposed system contains an album engine to create metadata and a photo player to consume the photos. It operates based on a standardized application format to encode a group of photos and associated metadata. Experiments showed that the proposed system achieved reasonable albuming performance.
Visualizing the Perceived Discomfort of Stereoscopic Video

Visual discomfort prediction is of importance for image safety issue in stereoscopic displays. This paper proposes automatic visualization of the perceived discomfort for stereoscopic video contents. The proposed method makes effective use of saliency-based measures for visual importance analysis in video scenes. Based on the analysis of visual importance, we quantify and visualize the visual discomfort induced by disparity and motion characteristics of stereoscopic video contents. The proposed method outputs visual importance-based comfort maps that allow users to monitor which regions in each video frame are perceptually significant and problematic with respect to visual discomfort. Experimental results have demonstrated the effectiveness of the proposed method by subjective assessments using various types of stereoscopic videos with diverse disparity and motion characteristics.
Privacy Protection in Video Surveillance Systems Using Scalable Video Coding

Thanks to high-speed internet access and feature-rich mobile devices, the demand for ubiquitous and secure surveillance systems has increased. In this paper, we propose a privacy-protected video surveillance system that makes use of Scalable Video Coding (SVC). SVC can be used to fulfill the requirement of omnipresence. Further, to address privacy concerns, we detect face regions and subsequently scramble these regions-of-interest (ROIs) in the compressed domain. To demonstrate the feasibility of the proposed video surveillance system, simulation results are provided. The results show that our system is able to provide a good level of security, while offering access to surveillance video content in heterogeneous usage environments.
Generation of conspicuity-improved synthetic image from digital breast tomosynthesis

Digital breast tomosynthesis (DBT) is a new 3-D imaging modality that alleviates the tissue overlap problem of mammography. Currently, DBT has been used in combination with full-field digital mammography (FFDM). Although some studies have shown the superiority of combination of DBT and FFDM in diagnosing the breast cancer, this combined procedure has the drawback that radiation doses are increased compared to the FFDM alone. In this paper, we propose a novel approach for generating 2-D synthetic image from 3-D DBT volume to eliminate the need for acquiring additional FFDM. The proposed approach utilizes the conspicuity of the voxels of 3-D DBT volume to improve the conspicuity of lesions in generating 2-D synthetic image. Comparative experiments have been conducted using clinical data set to investigate the quality of images generated by the proposed method. Experimental results demonstrated that the lesion conspicuity was the best in the synthetic image generated by proposed method.
Hierarchical rotational invariant similarity measurement for MPEG-7 homogeneous texture descriptor

A similarity-matching technique for a content-based image retrieval system derived from the MPEG-7 texture descriptor is described. This proposed technique is especially invariant with regard to the rotated images. For the rotational invariance, a hierarchical similarity measurement method was employed to simplify the computational complexity. To verify this method, experiments with MPEG-7 data sets were performed.
Semantic Quality for Content-Aware Video Adaptation

Recently, the concept of semantic transcoding has been introduced. However, there is little research on semantic quality measure to quantitatively guide and evaluate semantic transcoding. In this paper, we present a framework to formulate the semantic quality of an adapted video compared to the original one. Both original video and adapted video are represented by conceptual graphs, and then the similarities between the graphs are used to compute the overall semantic quality. Moreover, the dependence of quality on context is also discussed
Development of subjective quality based adaptation system for SVC

In this paper, we present the development of adaptation system for SVC, where adaptation decision is based on subjective quality. To achieve video adaptation which is accordant to human visual perception, the subjective quality metric is adopted into the adaptation system. Since the subjective quality metric is modeled on the subjective perception of a video content, the proposed system can maximize the quality of service (QoS) for the given adapted content. To verify the usefulness of the proposed adaptation system, subjective quality assessments were performed. The experimental result showed the proposed adaptation system was useful for visual perception adaptation.
Face detection for low power event detection in intelligent surveillance system

Recently, the development of intelligent surveillance system increasingly requires low power consumption. For the power saving, this paper presents an event detection function based on automatically detected human faces, which adaptively convert from low power camera mode to high performance camera mode. We propose an efficient face detection (FD) method for operating under the low power camera mode. By employing two-stage structure (i.e., region-of-interest (ROI) selection and false positive (FP) reduction), the proposed FD method requires a very low computational complexity and memory requirements without sacrificing the face detection robustness. Experimental results demonstrated that the proposed FD could be implemented in low power video cameras with promising performance.
Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR

This paper discusses a privacy-protected video surveillance system that makes use of JPEG extended range (JPEG XR). JPEG XR offers a low-complexity solution for the scalable coding of high-resolution images. To address privacy concerns, face regions are detected and scrambled in the transform domain, taking into account the quality and spatial scalability features of JPEG XR. Experiments were conducted to investigate the performance of our surveillance system, considering visual distortion, bit stream overhead, and security aspects. Our results demonstrate that subband-adaptive scrambling is able to conceal privacy-sensitive face regions with a feasible level of protection. In addition, our results show that subband-adaptive scrambling of face regions outperforms subband-adaptive scrambling of frames in terms of coding efficiency, except when low video bit rates are in use.
Leveraging an image folksonomy and the Signature Quadratic Form Distance for semantic-based detection of near-duplicate video clips

Being able to detect near-duplicate video clips (NDVCs) is a prerequisite for a plethora of multimedia applications. Given the observation that content transformations tend to preserve semantic information, techniques for NDVC detection may benefit from the use of a semantic approach. This paper discusses how an image folksonomy (i.e., community-contributed images and metadata) and the Signature Quadratic Form Distance (SQFD) can be leveraged for the purpose of identifying NDVCs. Experimental results obtained for the MIRFLICKR-25000 image set and the TRECVID 2009 video set indicate that an image folksonomy and SQFD can be successfully used for detecting NDVCs. In addition, our findings show that model-free NDVC detection (i.e., NDVC detection using an image folksonomy) has a higher semantic coverage than model-based NDVC detection (i.e., NDVC detection using the VIREO-374 semantic concept models).
Collaborative Face Recognition for Improved Face Annotation in Personal Photo Collections Shared on Online Social Networks

Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.
Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume

In this paper, we propose bilateral features for classifying breast masses by extracting the asymmetric information of both the left and the right breasts in the digital breast tomosynthesis (DBT) reconstructed volume. Clinically, it is known that the left and the right breast of the same patient tend to present a high degree of symmetry of internal structures over broad areas. On the other hand, masses appear as asymmetric densities which show different breast tissue structures between the left and the right breasts. Based on that clinical fact, bilateral features are proposed to measure the dissimilarity of texture or intensity characteristics between volumes-of-interest (VOIs) in a given breast, and the corresponding VOIs in the bilateral breast. Experimental results show that the proposed bilateral features in conjunction with single-view mass features can achieve higher level of classification performance in terms of the area under the receiver operating characteristic (ROC) curve (AUC) compared to the performance of the single-view features only.
Sparse Representation-Based Human Action Recognition Using an Action Region-Aware Dictionary

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. Conventional vision-based systems for human action recognition require the use of segmentation in order to achieve an acceptable level of recognition effectiveness. However, generic techniques for automatic segmentation are currently not available yet. Therefore, in this paper, we propose a novel sparse representation-based method for human action recognition, taking advantage of the observation that, although the location and size of the action region in a test video clip is unknown, the construction of a dictionary can leverage information about the location and size of action regions in training video clips. That way, we are able to segment, implicitly, action and context information in a test video clip, thus improving the effectiveness of classification. That way, we are also able to develop a context-adaptive classification strategy. As shown by comparative experimental results obtained for the UCF Sports Action data set, the proposed method facilitates effective human action recognition, even when testing does not rely on explicit segmentation.
Modality-bridge transfer learning for medical image classification

This paper presents a new approach of transfer learning-based medical image classification to mitigate insufficient labeled data problem in medical domain. Instead of direct transfer learning from source to small number of labeled target data, we propose a novel modality-bridge transfer learning which employs the bridge database in the same medical imaging acquisition modality as target database. By learning the projection function from source to bridge and from bridge to target, the domain difference between source (e.g., natural images) and target (e.g., X-ray images) can be mitigated. Experimental results show that the proposed method can achieve a high classification performance even for a small number of labeled target medical images, compared to various transfer learning approaches.
Multi-Factor Authentication Using Fingerprints and User-Specific Random Projection

Alarming increase in identity theft cases calls for the use of secure authentication systems that can clearly distinguish between authorized users and unauthorized users who are in possession of valid security tokens or passwords. To this end, we propose a multi-factor authentication system using user-specific pseudo-random numbers and fingerprints to generate revocable and privacy preserving biometric templates, which in turn are used for authentication. We evaluated the performance of the proposed system on the publicly available Fingerprint Verification Competition (FVC) 2000 database using Receiver Operating Characteristic (ROC) curves. Experimental results show that Equal Error Rates (EER) less than 0.4% can be achieved.
Effect of Stimulus Width on the Perceived Visual Discomfort in Viewing Stereoscopic 3-D-TV

To address the issues of viewing safety in stereoscopic three-dimensional television, it is important to investigate determinants of visual discomfort in viewing stereoscopic images. In stereoscopic viewing, it is well known that visual discomfort can be induced by excessive binocular disparity, fast changes in binocular disparity, and binocular asymmetry. However, subjective sensation of visual discomfort could also be substantially different depending on other characteristics of visual stimulus given the complexity of the visual system. From previous studies that have investigated the relation between stimulus characteristics and binocular fusion limit, we speculate that stimulus width (i.e., object width in the stereoscopic scene) can also affect the perceived visual discomfort of stereoscopic images. This paper investigates the effect of stimulus width on visual discomfort by measuring subjective visual discomfort and binocular fusion time. Experimental results show that smaller stimulus width could induce more visual discomfort and increase binocular fusion time.
Attention model-based visual comfort assessment for stereoscopic depth perception

This paper investigates visual discomfort induced by depth perception for viewing stereoscopic 3D images. In order to assess the degree of visual discomfort, we exploit an attention model-based visual comfort metric, considering perceptually significant regions of stereoscopic images. In particular, the relationship between visual comfort and depth perception is modeled based on psychophysical experiments with diverse amount of binocular disparities. Then, several spatial pooling techniques are applied using a visual comfort model in order to investigate the feasibility of the attention-model based visual comfort metric. Subjective assessments have been conducted using real stereoscopic 3D images, demonstrating the efficiency of the attention model-based visual comfort metric.
Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition

Automatic facial expression recognition (FER) is becoming increasingly important in the area of affective computing systems because of its various emerging applications such as human-machine interface and human emotion analysis. Recently, sparse representation based FER has become popular and has shown an impressive performance. However, sparse representation could often produce less meaningful sparse solution for FER due to intra-class variation such as variation in identity or illumination. This paper proposes a new sparse representation based FER method, aiming to reduce the intra-class variation while emphasizing the facial expression in a query face image. To that end, we present a new method for generating an intra-class variation image of each expression by using training expression images. The appearance of each intra-class variation image could be close to the appearance of the query face image in identity and illumination. Therefore, the differences between the query face image and its intra-class variation images are used as the expression features for sparse representation. Experimental results show that the proposed FER method has high discriminating capability in terms of improving FER performance. Further, the intra-class variation images of non-neutral expressions are complementary with that of neutral expression, for improving FER performance.
Program segment based personalized broadcasting

Recently, a personalized service based on viewer's preference is available in digital broadcasting. It takes eventually aiming at user satisfactory personalized service in anytime and anywhere. Being more available in personalization, user is looking forward to consuming segments of program in his preference as well as entire program. In this paper, we propose a new broadcasting system to generate and provide segments-based personalized broadcasting service to user terminals. Selecting and filtering suitable segments in broadcasting programs are presented with the schema of TV-Anytime Forum(TVAF) metadata. The proposed system shows a scheme with which program segments are reorganized into personalized program, i.e., virtual program. To verify the usefulness of the proposed system, we demonstrated the virtual program, which was made with user preferred segments, on a test-bed which consists of service provider, user terminal and mobile terminal1.
Visual Comfort Amelioration Technique for Stereoscopic Images: Disparity Remapping to Mitigate Global and Local Discomfort Causes

This paper proposes a new disparity remapping framework to improve the visual comfort of stereoscopic images. The proposed framework adaptively remaps disparities of a scene according to different causes of visual discomfort. A linear disparity remapping is first performed in order to address visual discomfort induced by excessive disparities. This linear remapping changes the disparities of the scene to obtain an overall target disparity range. Then, a nonlinear disparity remapping process selectively adjusts the disparity of problematic local disparity ranges according to their contribution to the visual discomfort. The proposed nonlinear disparity remapping process enables us to minimize the loss in perceived depth range while further improving visual comfort. The effectiveness of the proposed disparity remapping framework has been successfully evaluated by subjective assessments of visual comfort and naturalness. Experimental results demonstrate the validity of the proposed remapping framework. More importantly, we show that the nonlinear refinement of disparity in problematic regions can efficiently improve visual comfort while maintaining the naturalness of the scene.
Video Event Filtering in Consumer Domain

A real-time content Altering system for live broadcasts in TV terminals enables a TV viewer to obtain desired video events from multiple channel broadcasts. In order to achieve stable and reliable event filtering for multiple video inputs, real-time filtering requirements such as frame sampling rate per channel, number of input channels, and buffer condition should be considered to meet the limited capacity of the terminal. In this paper, we propose a method of selecting those requirements by modeling a filtering system using a D/M/l queue. By minimizing the queueing time of input video frames, the proposed model can maximize filtering capacity for multiple video inputs in real-time. To verify the proposed model and analysis, we perform experiments on soccer videos with the proposed model. The experimental results show that the proposed model allows for more efficient choice of requirements within limited resources.
Visual comfort improvement in stereoscopic 3D displays using perceptually plausible assessment metric of visual comfort

Visual discomfort is caused by several factors such as accommodation-vergence conflict and window violation in conventional stereoscopic displays. To ensure comfortable conditions in the stereoscopic viewing, it is essential to develop an effective way of improving visual comfort in stereoscopic displays. This paper proposes a new method for visual comfort improvement that adjusts the disparity of a scene under the guidance of an objective assessment metric of visual comfort. The proposed method employs a visual comfort assessment metric that takes into account various discomfort factors in the stereoscopic viewing. The results of subjective assessment experiments demonstrate that the proposed method can efficiently improve visual comfort in the stereoscopic viewing.
Towards a Physiology-Based Measure of Visual Discomfort: Brain Activity Measurement While Viewing Stereoscopic Images With Different Screen Disparities

In this study, we tried to identify the cortical areas associated with the experience of visual discomfort in the viewing of stereoscopic images. To this purpose, we first conducted a subjective assessment experiment to select, based on viewers' opinions, two sets of stereoscopic video sequences: one comprising video sequences generally rated as comfortable and the other comprising video sequences generally rated as uncomfortable. The two sets differed mainly with respect to the absence or presence of excessive screen disparities. Next, we used these video sequences as stimuli in two functional magnetic resonance imaging (fMRI) experiments so that we could identify differences, if any, in brain activation while viewing comfortable and uncomfortable video sequences. We found that, compared to comfortable videos with small screen disparities, uncomfortable videos with excessive screen disparities resulted in significantly higher levels of activation in the following brain areas: the right middle frontal gyrus (MFG), the right inferior frontal gyrus (IFG), the right inferior parietal lobule (IPL), the right middle temporal gyrus (MTG), the bilateral lingual gyri, the bilateral cuneus, and the right precuneus. The results of the fMRI experiments suggest that visual discomfort due to excessive screen disparities can be caused by sensory (related with the subjective sensation of fusion difficulty and vision clarity) and/or motor (related with unnatural eye movements) phenomena. The intraparietal sulcus (IPS) regions in the human brain may be involved in the sensory phenomenon, and the frontal eye field (FEF), premotor cortex, and IPS regions may be involved in the motor phenomenon.