nils-herrmann/clean_scirepeval huggingface dataset:
Making Residual Vector Distribution Uniform for Distinctive Image Representation

Recently, image representation by vector of locally aggregated descriptors (VLADs) has been demonstrated to be super efficient in image representation. Due to the coarse division in the feature space, its discriminative power is limited. One intuitive way to address this issue is to construct a VLAD with a larger vocabulary, but this will lead to a higher dimensional VLAD and suffer more computational complexity when learning the principal component analysis parameters used to project VLAD onto a low-dimensional space. In this paper, we propose a hierarchical scheme to build the VLAD. In our approach, by generating some subwords to each visual word of a coarse vocabulary, a hidden layer visual vocabulary is constructed. With the hidden layer visual vocabulary, the feature space is divided finer. Then, we aggregate the residues in the hidden layer vocabulary to the coarse layer to obtain an image descriptor that is of the same dimension as the original VLAD. In addition, we reveal that performing the whitening operation to local descriptor can further enhance the discriminative power of the VLAD. We validate our approach with experiments mainly conducted on three benchmark data sets, i.e., Holidays data set, UKBench data set, and Oxford Building data set with Flickr1M as distractors and make comparison with the related algorithms on VLAD. The experimental results demonstrate the effectiveness of our algorithm.
Generating Descriptive Visual Words and Visual Phrases for Large-Scale Image Applications

Bag-of-visual Words (BoWs) representation has been applied for various problems in the fields of multimedia and computer vision. The basic idea is to represent images as visual documents composed of repeatable and distinctive visual elements, which are comparable to the text words. Notwithstanding its great success and wide adoption, visual vocabulary created from single-image local descriptors is often shown to be not as effective as desired. In this paper, descriptive visual words (DVWs) and descriptive visual phrases (DVPs) are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs. Since images are the carriers of visual objects and scenes, a descriptive visual element set can be composed by the visual words and their combinations which are effective in representing certain visual objects or scenes. Based on this idea, a general framework is proposed for generating DVWs and DVPs for image applications. In a large-scale image database containing 1506 object and scene categories, the visual words and visual word pairs descriptive to certain objects or scenes are identified and collected as the DVWs and DVPs. Experiments show that the DVWs and DVPs are informative and descriptive and, thus, are more comparable with the text words than the classic visual words. We apply the identified DVWs and DVPs in several applications including large-scale near-duplicated image retrieval, image search re-ranking, and object recognition. The combination of DVW and DVP performs better than the state of the art in large-scale near-duplicated image retrieval in terms of accuracy, efficiency and memory consumption. The proposed image search re-ranking algorithm: DWPRank outperforms the state-of-the-art algorithm by 12.4% in mean average precision and about 11 times faster in efficiency.
Adaptive synchronization of complex dynamical networks with two types of time-varying delays

This paper proposes a general complex dynamical network model with two types of time-varying delays. The model is more general than those in the existing literatures in that it contains nonlinear coupling term which implies that the network topology can be known or unknown. Besides, the two types of delays consist of the system delay at each node and the coupling delay between nodes. Based on the Lyapunov stability theory, an adaptive synchronization criterion is proposed theoretically. An illustrated example is given to show the effectiveness of the theoretical result.
Robust moving video object segmentation in the MPEG compressed domain

In this paper, we proposed a robust moving video object segmentation algorithm using features in the MPEG compressed domain. We first cluster the motion vectors and produce a motion mask. Then, a difference mask at 8 x 8 block size is extracted from the DC image by background subtraction method. Finally, the motion mask and the difference mask are combined conditionally to generate the final object mask based on a set of rules that are application specified and is obtained with learning or heuristic based methods. The experimental results show that this object segmentation scheme is more robust than those using DC image or motion vectors only.
Personalized Social Image Recommendation Method Based on User-Image-Tag Model

In the social image sharing websites (such as Flickr), users are allowed to upload images and tag them with tags. Due to the diversities of users’ interests, different users may tag the same image with different tags. Therefore, tags not only reveal some important image semantic clues, but also show user's preference, which can provide a new effective solution for overcoming the semantic gap as well as realizing a personalized recommendation. In this paper, a personalized social image recommendation method based on user-image-tag model is proposed. The main contributions of our work are 1) to efficiently make use of tags, social image tags are re-ranked according to the image content; 2) to obtain user preference, a user-image-tag model is constructed with tripartite graph according to the correlation among users, images and top-ranking tags; and 3) a personalized social recommendation system is implemented based on user-image-tag model. Experimental results proved that our method can significantly improve the accuracy of personalized image recommendation.
A security scheme based on time division multiplex

To achieve secure communication, two channels are required in traditional secure communication based on master-slave chaotic synchronization. The encrypted information is transmitted in one channel, and the signals used to synchronize the master-slave system are transmitted in the other one. The conventional scheme has a disadvantage that channel resources are not exploited efficiently. Therefore, a new system is proposed which uses a time division multiplexing technology to transmit information and synchronization signals in one channel, so that the efficiency of utilization of the channel resources doubles. The key issue studied in the paper is the allocation of time slots in this system, a detailed analysis is given, and simulation results are included to demonstrate the feasibility of this communication scheme in real applications.
Toward Robust Distance Metric Analysis for Similarity Estimation

In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.
A Saliency Guided Shallow Convolutional Neural Network for Traffic Signs Retrieval

As one of the important parts of road infrastructure, traffic signs provide vital information for road users. Achieving efficient traffic signs retrieval greatly contributes to the intelligent analysis on big traffic data. In this paper, we propose a saliency guided shallow convolutional neural network (CNN) for traffic signs accurate and fast retrieval. Firstly, by unifying deep saliency and hashing learning in a single architecture, the proposed CNN model performs joint learning in a point-wise manner, which is scalable on large-scale datasets. Then, deep saliency features and hashing-like outputs are extracted from traffic sign images with the saliency guided shallow CNN. The binarized hashing-like outputs together with saliency features are used to construct features database. Finally, a coarse to fine similarity measurement is performed by Euclidean distance and Hamming distance to return retrieval results. Experimental results demonstrate the retrieval accuracy of our method outperforms five state-of-the-art methods on GTSRB dataset.
Content-based medical image retrieval using dynamically optimized regional features

This paper proposes a content-based medical image retrieval (CBMIR) framework using dynamically optimized features from multiple regions of medical images. These regional features, including structural and statistical properties of color, texture and geometry, are extracted from multiple dominant regions segmented by applying Gaussian mixture modeling (GMM) and the expectation maximization (EM) algorithm to medical images. Over them, principal component analysis (PCA) is utilized to construct query templates and to reduce feature dimensions for representative feature optimization. Applying this method to the tasks of the medical imageCLEF 2004 we achieve better retrieval performance (MAP 0.4535) over the existing work on casImage of about 9000 images.
Image-Specific Classification With Local and Global Discriminations

Most image classification methods try to learn classifiers for each class using training images alone. Due to the interclass and intraclass variations, it would be more effective to take the testing images into consideration for classifier learning. In this brief, we propose a novel image-specific classification method by combing the local and global discriminations of training images. We adaptively train classifier for each testing image instead of generating classifiers for each class with training images alone. For each testing image, we first select its  ${k}$  nearest neighbors in the training set with the corresponding labels for local classifier training. This helps to model the distinctive characters of each testing image. Besides, we also use all the training images for global discrimination modeling. The local and global discriminations are combined for final classification. In this way, we could not only model the specific character of each testing image but also avoid the local optimum by jointly considering all the training images. To evaluate the usefulness of the proposed image-specific classification with local and global discrimination (ISC-LG) method, we conduct image classification experiments on several public image data sets. The superior performances over other baseline methods prove the effectiveness of the proposed ISC-LG method.
Graph-guided sparse reconstruction for region tagging

Many of contextual correlations co-exist within the segmented regions among images, like the visual context and semantic context. The appropriate integration and utilization of such contexts are very important to boost the performance of region tagging. Inspired by the recent advances of sparse reconstruction methods, this paper proposes an approach, called Graph-Guided Sparse Reconstruction for Region Tagging (G2SRRT). The G2SRRT consists of two steps: sparse reconstruction for testing regions and tag propagation from training regions to testing regions. In G2SRRT, graph is conducted to flexibly model the contextual correlations among regions. To integrate the graph structure learned from training regions into the sparse reconstruction, we define a Graph-Guided Fusion (G2F) penalty over the graph to encourage the sparsity of differences between two reconstruction coefficients, which corresponds to the linked regions in the graph. Guided by this G2F penalty, the highly correlated regions tend to be jointly selected for the reconstruction, which results in a better performance of region tagging. Experiments on three open benchmark image datasets demonstrate the effectiveness of the proposed algorithm.
A secure and robust approach to scalable video authentication

In this paper, we present a secure and robust content authentication scheme for scalable video streaming. In our authentication scheme we consider three common video transcoding methods as acceptable content manipulations, when the streaming bit-rate needs to be reduced, namely frame resizing, frame dropping and multi-cycle coding. By employing error correction coding (ECC) in different ways, the proposed scheme is insensitive to those incidental distortions introduced during the transcoding (i.e., robust) while is still sensitive to other intentional distortions such as frame alterations and insertion (i.e., secure). One key feature in our scheme is that it achieves an end-to-end authentication independent of transcoding infrastructure and obtains a good compromise between system robustness and security.
Homotopy-Enhanced Power Flow Methods for General Distribution Networks With Distributed Generators

The issue of power flow divergence when dispersed generations are modeled as P-V nodes needs to be addressed. To address this issue, this paper presents a homotopy-enhanced numerical power flow method. Several numerical aspects of the implementation were discussed. To evaluate its reliability and effectiveness, the proposed method is applied to several standard test systems, a practical 1101-node distribution network and the IEEE 8500-node test system. The evaluation results were very promising. In addition, one distinguished feature of the homotopy-enhanced numerical power flow method is that it is applicable to several existing power flow methods.
A novel adaptive motion detection based on k-means clustering

Detecting a high-quality moving object with good robustness in computer vision system has important significance for follow-up task. Researching on the traditional algorithm, this paper proposes a background reconstruction algorithm based on a modified k-means clustering and the Single Gaussian model which could provide an accurate background image through a sequence of scene images with foreground objects. Then based on the statistical characteristics of the background pixels region detects the moving object. Aiming to the effect of dynamic changes of the environment, this paper proposes a method of robust adaptive motion detection Combined with the principle of Mathematical Morphology and Region-labeling. Experiments prove this method can complete the task of moving object detection in complex environment.
Replay Scene Classification in Soccer Video Using Web Broadcast Text

The automatic extraction of sports video highlights is a typical kind of personalized media production process. Many ways have been studied from the viewpoints of low-level audio/visual processing (e.g. detection of excited commentator speech), event detection (e.g. goal detection), etc. However, the subjectivity of highlights is an unavoidable bottleneck. The replay scene is an effective clue for highlights in broadcast sports video due to the incorporation of video production knowledge. Most related work deals with the replay detection and/or a simple composition of all detected replays to generate highlights. Different from previous work, our work considers different flavors of different people in terms of highlight content or type through replay scenes classification. The main contributions include: 1) proposing a multi-modal (visual+textual) approach for refined replay classification; 2) employing the sources of broadcast Web text (BWT) to facilitate replay content analysis. An overall accuracy of 79.9% has been achieved on seven soccer matches over seven replay categories
MiBank: A web-based integrated medical information system for traumatic brain injury

Approximately ten million people in the world suffer from traumatic brain injury (TBI) each year. A total of $60 billion cost due to TBI was estimated in the United States in year 2000. To reduce the burden more clinical research and education are required. In this study we developed MiBank, a web-based integrated TBI information system, to enable rapid access to both digital images and associated text reports for audit, education and research. MiBank contains more than 30,000 brain computed tomography (CT) images from over 500 patients and is equipped with functional options to search, compare, summarize and annotate CT images, radiology reports and clinician remarks online. The image annotation function is designed to enable clinicians and researchers to capture and display domain expert knowledge, and a discussion forum function encourages active communication and sharing. Emphasizing confidentiality of anonymised data and access control, MiBank provides a virtual collaboration platform integrating various clinical data sets for research and continuing education. As an online information system, it eliminates the restrictions of the traditional isolated DICOM workstations. MiBank can potentially support remote consulting and statistical analysis of aggregated multimodality data. Although MiBank is designed and implemented for TBI, it may be extended and customized to study other clinical disorders. In this report, we share our learning experience through user survey and also propose a future plan to improve the system. MiBank may be accessible by researchers and clinicians on request.
Inferring Emotional Tags From Social Images With User Demographics

Social images, which are images uploaded and shared on social networks, are used to express users’ emotions. Inferring emotional tags from social images is of great importance; it can benefit many applications, such as image retrieval and recommendation. Whereas previous related research has primarily focused on exploring image visual features, we aim to address this problem by studying whether user demographics make a difference regarding users’ emotional tags of social images. We first consider how to model the emotions of social images. Then, we investigate how user demographics, such as gender, marital status, and occupation, are related to the emotional tags of social images. A partially labeled factor graph model named the demographics factor graph model (<bold>D-FGM</bold> ) is proposed to leverage the uncovered patterns. Experiments on a data set collected from the world's largest image sharing website Flickr<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label> <p>[Online]. Available: <uri>http://www.flickr.com/</uri></p></fn> confirm the accuracy of the proposed model. We also find some interesting phenomena. For example, men and women have different patterns to tag “anger” for social images.
Angular-Similarity-Preserving Binary Signatures for Linear Subspaces

We propose a similarity-preserving binary signature method for linear subspaces. In computer vision and pattern recognition, linear subspace is a very important representation for many kinds of data, such as face images, action and gesture videos, and so on. When there is a large amount of subspace data and the ambient dimension is high, the cost of computing the pairwise similarity between the subspaces would be high and it requires a large storage space for storing the subspaces. In this paper, we first define the angular similarity and angular distance between the subspaces. Then, based on this similarity definition, we develop a similarity-preserving binary signature method for linear subspaces, which transforms a linear subspace into a compact binary signature, and the Hamming distance between two signatures provides an unbiased estimate of the angular similarity between the two subspaces. We also provide a lower bound of the signature length sufficient to guarantee uniform distance-preservation between every pair of subspaces in a set. Experiments on face recognition, gesture recognition, and action recognition verify the effectiveness of the proposed method.
Heterogeneous Graph Propagation for Large-Scale Web Image Search

State-of-the-art web image search frameworks are often based on the bag-of-visual-words (BoVWs) model and the inverted index structure. Despite the simplicity, efficiency, and scalability, they often suffer from low precision and/or recall, due to the limited stability of local features and the considerable information loss on the quantization stage. To refine the quality of retrieved images, various postprocessing methods have been adopted after the initial search process. In this paper, we investigate the online querying process from a graph-based perspective. We introduce a heterogeneous graph model containing both image and feature nodes explicitly, and propose an efficient reranking approach consisting of two successive modules, i.e., incremental query expansion and image-feature voting, to improve the recall and precision, respectively. Compared with the conventional reranking algorithms, our method does not require using geometric information of visual words, therefore enjoys low consumptions of both time and memory. Moreover, our method is independent of the initial search process, and could cooperate with many BoVW-based image search pipelines, or adopted after other postprocessing algorithms. We evaluate our approach on large-scale image search tasks and verify its competitive search performance.
Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification

We propose Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) to address the problem of person re-identification on multi-cameras. Re-identifications on different cameras are considered as related tasks, which allows the shared information among different tasks to be explored to improve the re-identification accuracy. The MTL-LORAE framework integrates low-level features with mid-level attributes as the descriptions for persons. To improve the accuracy of such description, we introduce the low-rank attribute embedding, which maps original binary attributes into a continuous space utilizing the correlative relationship between each pair of attributes. In this way, inaccurate attributes are rectified and missing attributes are recovered. The resulting objective function is constructed with an attribute embedding error and a quadratic loss concerning class labels. It is solved by an alternating optimization strategy. The proposed MTL-LORAE is tested on four datasets and is validated to outperform the existing methods with significant margins.