nils-herrmann/clean_scirepeval huggingface dataset:
Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis

Computer-aided diagnosis (CAD) systems in gray-scale breast ultrasound images have the potential to reduce unnecessary biopsy of breast masses. The purpose of our study is to develop a robust CAD system based on the texture analysis. First, gray-scale invariant features are extracted from ultrasound images via multi-resolution ranklet transform. Thus, one can apply linear support vector machines (SVMs) on the resulting gray-level co-occurrence matrix (GLCM)-based texture features for discriminating the benign and malignant masses. To verify the effectiveness and robustness of the proposed texture analysis, breast ultrasound images obtained from three different platforms are evaluated based on cross-platform training/testing and leave-one-out cross-validation (LOO-CV) schemes. We compare our proposed features with those extracted by wavelet transform in terms of receiver operating characteristic (ROC) analysis. The AUC values derived from the area under the curve for the three databases via ranklet transform are 0.918 (95% confidence interval [CI], 0.848 to 0.961), 0.943 (95% CI, 0.906 to 0.968), and 0.934 (95% CI, 0.883 to 0.961), respectively, while those via wavelet transform are 0.847 (95% CI, 0.762 to 0.910), 0.922 (95% CI, 0.878 to 0.958), and 0.867 (95% CI, 0.798 to 0.914), respectively. Experiments with cross-platform training/testing scheme between each database reveal that the diagnostic performance of our texture analysis using ranklet transform is less sensitive to the sonographic ultrasound platforms. Also, we adopt several co-occurrence statistics in terms of quantization levels and orientations (i.e., descriptor settings) for computing the co-occurrence matrices with 0.632+ bootstrap estimators to verify the use of the proposed texture analysis. These experiments suggest that the texture analysis using multi-resolution gray-scale invariant features via ranklet transform is useful for designing a robust CAD system.
Enhanced canonical correlation analysis with local density for cross-domain visual classification

Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed from each domain. As a result, our CCA not only exhibits excellent abilities in identifying representative data, noisy data like outliers can be further suppressed during the derivation of our CCA subspace. In our experiments, we successfully apply the proposed methods for solving two cross-domain classification tasks: person re-identification and cross-view action recognition.
Soft-decision hierarchical classification using SVM-type classifiers

In this paper, we address both recognition of true object classes and rejection of false (non-object) classes as occurs in many realistic pattern recognition problems. We modified our hierarchical binary-decision classifier to produce analog outputs at each node, with values proportional to the class conditional probabilities at that node. This yields a new soft-decision hierarchical system. The hierarchical classification structure is designed by our weighted support vector k-means clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. Compared to the standard SVM, use of the Gaussian kernel function and a looser constraint in the classifier design give our SVRDM an improved rejection ability. The soft-decision SVRDM output allows us to use the confidence level of each class to improve the classification (for true class inputs) and rejection (for false class inputs) performance of the hierarchical classifier. False class rejection is a major new aspect of this work. It is not present in most prior work. Excellent test results on a real infra-red (IR) database are presented.
Multi-view Nonnegative Matrix Factorization for Clothing Image Characterization

Due to the ambiguity in describing and discriminating between clothing images of different styles, it has been a challenging task to solve clothing image characterization problems. Based on the use of multiple types of visual features, we propose a novel multi-view nonnegative matrix factorization (NMF) algorithm for solving the above task. Our multi-view NMF not only observes image representations for describing clothing images in terms of visual appearances, an optimal combination of such features for each clothing image style would also be learned, while the separation between different image styles can be preserved. To verify the effectiveness of our method, we conduct experiments on two image datasets, and we confirm that our method produces satisfactory performance in terms of both clustering and categorization.
A hierarchical classifier using new support vector machine

A binary hierarchical classifier is proposed to solve the multi-class classification problem. We also require rejection of non-target inputs, which produces a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is considered at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform features, initial recognition and rejection test results on simulated infrared data are excellent.
Query-Adaptive Multiple Instance Learning for Video Instance Retrieval

Given a query image containing the object of interest (OOI), we propose a novel learning framework for retrieving relevant frames from the input video sequence. While techniques based on object matching have been applied to solve this task, their performance would be typically limited due to the lack of capabilities in handling variations in visual appearances of the OOI across video frames. Our proposed framework can be viewed as a weakly supervised approach, which only requires a small number of (randomly selected) relevant and irrelevant frames from the input video for performing satisfactory retrieval performance. By utilizing frame-level label information of such video frames together with the query image, we propose a novel query-adaptive multiple instance learning algorithm, which exploits the visual appearance information of the OOI from the query and that of the aforementioned video frames. As a result, the derived learning model would exhibit additional discriminating abilities while retrieving relevant instances. Experiments on two real-world video data sets would confirm the effectiveness and robustness of our proposed approach.
Cross-layer design for video streaming with dynamic antenna selection

Spatial multiplexing is an efficient transmission technique for video streaming in multiple-input multiple-output (MIMO) wireless communication links due to its capability to support the high transmit rate. Nevertheless, the video quality is sensitive to packet losses resulting from channel fading and co-interference between transmit antennas in spatial multiplexing systems. Antenna selection techniques have been investigated to balance the spatial multiplexing gain and the link reliability. Herein, a novel cross-layer framework using dynamic antenna selection is proposed to control the multiplexing-diversity tradeoff with joint error-resilient source and channel coding. The cross-layer optimization is performed to optimize the physical, link, and application layers, and to minimize the end-to-end video distortion.
Seeing through the appearance: Body shape estimation using multi-view clothing images

We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models at each view of interest, our work uniquely associates the anthropometric measurements (e.g., body height or leg length) across different views. For performing body shape estimation using multi-view clothing images, the proposed algorithm solves an optimization task which recovers the body shape with image and measurement reconstruction guarantees. In the experiments, we will show that the use of our proposed method would achieve satisfactory estimation results, and performs favorably against single-view or other baseline approaches for both body shape and measurement estimation.
Partial image blur detection and segmentation from a single snapshot

In this paper, we address the problem of detecting and segmenting partial image blur from a single input image. Instead of assuming particular image priors or requiring additional user annotation, we propose a novel learning framework which jointly solves the tasks of blur kernel estimation and image blur segmentation, so that partial image blur can be automatically separated from the remaining parts of the input image. By alternating between the two learning tasks, we show that our proposed method would achieve promising detection and segmentation performance, which would benefit further processing or analysis tasks of interest. We also verify that, via both qualitative and quantitative evaluation, our approach would perform favorably against state-of-the-art blur detection or segmentation works.
Robust anchorperson detection based on audio streams using a hybrid I-vector and DNN system

Anchorperson segment detection enables efficient video content indexing for information retrieval. Anchorperson detection based on audio analysis has gained popularity due to lower computational complexity and satisfactory performance. This paper presents a robust framework using a hybrid I-vector and deep neural network (DNN) system to perform anchorperson detection based on audio streams of video content. The proposed system first applies I-vector to extract speaker identity features from the audio data. With the extracted speaker identity features, a DNN classifier is then used to verify the claimed anchorperson identity. In addition, subspace feature normalization (SFN) is incorporated into the hybrid system for robust feature extraction to compensate the audio mismatch issues caused by recording devices. An anchorperson verification experiment was conducted to evaluate the equal error rate (EER) of the proposed hybrid system. Experimental results demonstrate that the proposed system outperforms the state-of-the-art hybrid I-vector and support vector machine (SVM) system. Moreover, the proposed system was further enhanced by integrating SFN to effectively compensate the audio mismatch issues in anchorperson detection tasks.
Exploring Visual and Motion Saliency for Automatic Video Object Extraction

This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.
With one look: 3D face shape estimation from a single snapshot

Estimating the 3D shape information of a face from a single image is a challenging task, especially when the input image is captured under unconstrained scenarios (e.g., variations of pose, illumination, expression, or even disguise). Previous approaches to this problem typically require careful initialization, registration, or segmentation of the face image regions. With the objective to match the detected landmarks of the input image with those of a set of reference 3D models, we propose a non-negative least squares (NNLS) based algorithm for joint pose and shape estimation. With the additional imposed pose regularization, our method is able to perform person-specific shape estimation, while the camera pose can be simultaneously recovered. We show that our method is robust, effective, and computationally feasible. Moreover, it would perform favorably against existing approaches to 3D shape estimation from a single unconstrained image.
Joint trilateral filtering for depth map super-resolution

Depth map super-resolution is an emerging topic due to the increasing needs and applications using RGB-D sensors. Together with the color image, the corresponding range data provides additional information and makes visual analysis tasks more tractable. However, since the depth maps captured by such sensors are typically with limited resolution, it is preferable to enhance its resolution for improved recognition. In this paper, we present a novel joint trilateral filtering (JTF) algorithm for solving depth map super-resolution (SR) problems. Inspired by bilateral filtering, our JTF utilizes and preserves edge information from the associated high-resolution (HR) image by taking spatial and range information of local pixels. Our proposed further integrates local gradient information of the depth map when synthesizing its HR output, which alleviates textural artifacts like edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.
Segmentation-free object localization in image collections

We propose a novel method to address object localization in a weakly supervised framework. Unlike prior work using exhaustive search methods such as sliding windows, we advocate the use of visual attention maps which are constructed by class-specific visual words. Based on dense SIFT descriptors, these visual words are selected by support vector machines and feature ranking techniques. Therefore, discriminative information is learned and embedded in these visual words. We further refine the constructed map by Gaussian smoothing and cross bilateral filtering to preserve local spatial information of the objects. Very promising localization results are reported on a subset of the Caltech-256 dataset, and our method is shown to improve the state-of-the-art recognition performance using the bag-of-feature (BOF) model.
Example-Based Image Textural Style Transfer

Textural style transfer aims to transfer the textural style identified from a reference image to a source image, while retaining the scene of the source image. This article proposes a context-aware style transfer algorithm based on sparse-representation-based textural synthesis. Whereas sparse representation is designed to extract the style component of the exemplar image, textural synthesis is performed in a context-aware setting to preserve the original scene structure of the source image. Unlike existing solutions that require prior knowledge of the textural style of interest or user interaction, this method performs the transfer automatically. Experimental results demonstrate the effectiveness of the proposed method for automatic style transfer from a single style template image that is not accompanied with its original real image.
Simultaneous Object Recognition and Localization in Image Collections

This papers presents a weakly supervised method to simultaneouslyaddress object localization and recognitionproblems. Unlike prior work using exhaustive search methodssuch as sliding windows, we propose to learn categoryand image-specific visual words in image collections by extractingdiscriminating feature information via two differenttypes of support vector machines: the standard L2-regularized L1-loss SVM, and the one with L1 regularizationand L2 loss. The selected visual words are used toconstruct visual attention maps, which provide descriptiveinformation for each object category. To preserve local spatialinformation, we further refine these maps by Gaussiansmoothing and cross bilateral filtering, and thus both appearanceand spatial information can be utilized for visualcategorization applications. Our method is not limited toany specific type of image descriptors, or any particularcodebook learning and feature encoding techniques. In thispaper, we conduct preliminary experiments on a subset ofthe Caltech-256 dataset using bag-of-feature (BOF) modelswith SIFT descriptors. We show that the use of our visual attentionmaps improves the recognition performance, whilethe one selected by L1-regularized L2-loss SVMs exhibitsthe best recognition and localization results.
Self-learning approach to color demosaicking via support vector regression

Most digital cameras capture one primary color at each pixel by a single sensor overlaid with a color filter array. To recover a full color image from incomplete color samples, one needs to restore the two missing color values for each pixel. This restoration process is known as color demosaicking. In this paper, we present a novel self-learning approach to this problem via support vector regression. Unlike prior learning-based demosaicking methods, our approach aims at extracting image-dependent information in constructing the learning model, and we do not require any additional training data. Experimental results show that our proposed method outperforms many state-of-the-art techniques in both subjective and objective image quality measures.
Detecting Rare Actions and Events from Surveillance Big Data with Bag of Dynamic Trajectories

Surveillance video is increasingly becoming the "biggest big data". This presents an unprecedented challenge for analyzing and mining the meaningful information (e.g., Rare actions or events) in such a huge amount of videos. Recent studies have shown that feature-trajectories-based methods are effective to encode motion information in video, consequently demonstrating superior performance in action and event detection. However, in existing methods, distance between two trajectories is often measured by linear models, which may be not robust enough when the lengths of trajectories are variable. Moreover, due to the rare distribution of target actions or events, the traditional classifier often tends to identify all samples as negative, consequently producing heavy performance bias. To address both two issues, this paper proposes a trajectory descriptor, BoDT (Bag of Dynamic Trajectories), and a multi-channel uneven SVM. By utilizing the DTW (dynamic time warping) algorithm to measure the similarity between two trajectories, BoDT is robust for variable-length trajectory representation. Meanwhie, as an extension of SVM with uneven margins, the proposed multi-channel uneven SVM can successfully identify rare events by adjusting a margin parameter to make the classification boundary properly moved away from the positive training examples. Extensive experiments on several benchmark datasets including KTH, YouTube, Olympic, MIT, QMUL and TRECVid demonstrate that our approach is feasible and effective.
A multimodal video copy detection approach with sequential pyramid matching

Content-based video copy detection over large corpus with complex transformations is important but challenging. It is not surprising that most existing methods fall short of either sufficient robustness to detect severely deformed copies or high accuracy to localize copy segments. In this paper, we propose a video copy detection approach which exploits complementary audio-visual features and sequential pyramid matching (SPM). Several independent detectors first match visual key frames or audio clips using individual features, and then aggregate the frame level results into video level results with SPM, which calculates video similarities by sequence matching at multiple granularities. Finally, detection results from basic detectors are fused and further filtered to generate the final result. Excellent performance evaluated on TRECVid 2010 copy detection task demonstrates the effectiveness of our approach.
Single and Multiple View Detection, Tracking and Video Analysis in Crowded Environments

In this paper, we present our detection, tracking and event recognition methods and the results for PETS 2012. First, ROIs (Regions of Interest) based on geometric constraints are utilized in single view detection to eliminate the negative influence of clutter environment. Then, an optimized observation model is applied to address the ID switching or tracking drifting problem in single view tracking. Third, we introduce the multi-view Bayesian network (MBN) to reduce the "phantom" phenomena which frequently happen in general multi-view detection tasks. At last, a motion-based event recognition method is proposed to handle the event recognition task. Experimental results on the PETS 2012 dataset indicate that our methods are very promising.
Deep hashing with multi-task learning for large-scale instance-level vehicle search

Hashing is a hot research topic in large-scale image search, due to its low memory cost and fast search speed. Recently, deep hashing, which adapts deep convolutional neural networks into hashing, has attracted much attention. In this paper, we propose a new supervised deep hashing method to deal with large-scale instance-level vehicle search, and make the following contributions. Firstly, multi-task learning is employed to learn the hash code, which exploits the available multiple labels of each vehicle, i.e., ID, model, and color. Secondly, differing from several deep hashing methods, which utilize sigmoid or tanh as the activation function of the hash layer, rectified linear unit is adopted in this paper and shows better performance. Thirdly, taking GoogLeNet as the base network, we show that search performance can be promoted significantly, by learning the network's parameters from scratch on our vehicle data. Finally, we perform extensive experiments on a large-scale dataset with up to one million vehicles. The experimental results demonstrate the effectiveness of the proposed method, which outperforms single task deep hashing methods with classification and triplet ranking losses, respectively.
Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple Auto-Encoders

In this paper, we present a novel framework to detect abnormal behaviors in surveillance videos by using fuzzy clustering and multiple Auto-Encoders (FMAE). As detecting abnormal behaviors is often treated as an unsupervised task, how to describe normal patterns becomes the key point. Considering there are many types of normal behaviors in the daily life, we use the fuzzy clustering technique to roughly divide the training samples into several clusters so that each cluster stands for a normal pattern. Then we deploy multiple Auto-Encoders to estimate these different types of normal behaviors from weighted samples. When testing on an unknown video, our framework can predict whether it contains abnormal behaviors or not by summarizing the reconstruction cost through each Auto-Encoder. Since there are always lots of redundancies in the surveillance video, Auto-Encoder is a pretty good tool to capture common structures of normal video sequences automatically as well as estimate normal patterns. The experimental results show that our approach achieves good performance on three public video analysis datasets and statistically outperforms the state-of-the-art approaches under some scenes.