nils-herrmann/clean_scirepeval huggingface dataset:
The application of BI-RADS feature in the ultrasound breast tumor CAD system

The breast cancer is one of the most common diseases in women. This paper proposed a breast tumor computer aided diagnosis (CAD) system utilized the Breast Imaging Reporting and Data System (BI-RADS) features. The BI-RADS feature scoring scheme is designed to transform the BI-RADS report to a vector. And the decision tree algorithm is adopted to classify the vector. Compared with previous CAD system, the proposed system is easier to be understood by the clinician. Without the image preprocessing, the proposed system can be applied in different ultrasound machines. There are 440 samples collected from the Cancer Center of Sun Yat-sen University. In the experiment, the five-fold cross validation is employed to evaluate the proposed system. The result shows that the performance of the proposed system is better than the CAD method which takes the BI-RADS feature as a guide to extract features from images. The average accuracy achieves 89.38%, specificity is 90.74%, sensitivity is 86.18%, positive predictive value (PPV) reaches 93.57% and negative predictive value (NVP) is 79.82%.
Detection of Sudden Pedestrian Crossings for Driving Assistance Systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.
Robust Discrete Spectral Hashing for Large-Scale Image Semantic Indexing

In big data era, the ever-increasing image data has posed significant challenge on modern image retrieval. It is of great importance to index images with semantic keywords efficiently and effectively, especially confronted with fast-evolving property of the web. Learning-based hashing has shown its power in handling large-scale high-dimensional applications, such as image retrieval. Existing solutions normally separate the process of learning binary codes and hash functions into two independent stages to bypass challenge of the discrete constraints on binary codes. In this work, we propose a novel unsupervised hashing approach, namely robust discrete hashing (RDSH), to facilitate large-scale semantic indexing of image data. Specifically, RDSH simultaneously learns discrete binary codes as well as robust hash functions within a unified model. In order to suppress the influence of unreliable binary codes and learn robust hash functions, we also integrate a flexible `2;p loss with nonlinear kernel embedding to adapt to different noise levels. Finally, we devise an alternating algorithm to efficiently optimize RDSH model. Given a test image, we first conduct r-nearest-neighbor search based on Hamming distance of binary codes, and then propagate semantic keywords of neighbors to the test image. Extensive experiments have been conducted on various real-world image datasets to show its superiority to the state-of-the-arts in large-scale semantic indexing.
Learning a Probabilistic Topology Discovering Model for Scene Categorization

A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regions (e.g., 4 × 4 patches) within an image. In this paper, we mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization. In particular, by decomposing a scene image into basic components, a graphlet model is used to describe their spatial interactions. Accordingly, scene categorization is formulated as an intergraphlet matching problem. The above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes. The selected graphlets are highly discriminative and independent, characterizing the topological characteristics of scene images. A weak learner is subsequently trained for each topology, which are boosted together to jointly describe the scene image. In our experiment, the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories, and our proposed method beats state-of-the-art models on five popular scene data sets.
Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud

With the rapid development of the cloud computing and mobile service, users expect a better experience through multimedia computing, such as automatic or semi-automatic personal image and video organization and intelligent user interface. These functions heavily depend on the success of image understanding, and thus large-scale image annotation has received intensive attention in recent years. The collaboration between mobile and cloud opens a new avenue for image annotation, because the heavy computation can be transferred to the cloud for immediately responding user actions. In this paper, we present a scheme for image annotation on the cloud, which transmits mobile images compressed by Hamming compressed sensing to the cloud and conducts semantic annotation through a novel Hessian regularized support vector machine on the cloud. We carefully explained the rationality of Hessian regularization for encoding the local geometry of the compact support of the marginal distribution and proved that Hessian regularized support vector machine in the reproducing kernel Hilbert space is equivalent to conduct Hessian regularized support vector machine in the space spanned by the principal components of the kernel principal component analysis. We conducted experiments on the PASCAL VOC'07 dataset and demonstrated the effectiveness of Hessian regularized support vector machine for large-scale image annotation.
Locality Adaptive Discriminant Analysis for Spectral–Spatial Classification of Hyperspectral Images

Linear discriminant analysis (LDA) is a popular technique for supervised dimensionality reduction, but with less concern about a local data structure. This makes LDA inapplicable to many real-world situations, such as hyperspectral image (HSI) classification. In this letter, we propose a novel dimensionality reduction algorithm, locality adaptive discriminant analysis (LADA) for HSI classification. The proposed algorithm aims to learn a representative subspace of data, and focuses on the data points with close relationship in spectral and spatial domains. An intuitive motivation is that data points of the same class have similar spectral feature and the data points among spatial neighborhood are usually associated with the same class. Compared with traditional LDA and its variants, LADA is able to adaptively exploit the local manifold structure of data. Experiments carried out on several real hyperspectral data sets demonstrate the effectiveness of the proposed method.
Biclustering Learning of Trading Rules

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood (K-NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC-K-NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.
Compressed Hashing

Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long code-words) are required. To address this challenge, in this paper we propose a novel approach called Compressed Hashing by exploring the techniques of sparse coding and compressed sensing. In particular, we introduce as parse coding scheme, based on the approximation theory of integral operator, that generate sparse representation for high dimensional vectors. We then project s-parse codes into a low dimensional space by effectively exploring the Restricted Isometry Property (RIP), a key property in compressed sensing theory. Both of the theoretical analysis and the empirical studies on two large data sets show that the proposed approach is more effective than the state-of-the-art hashing algorithms.
Person Re-identification by Multi-hypergraph Fusion

Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts.
Joint Multilabel Classification With Community-Aware Label Graph Learning.

As an important and challenging problem in machine learning and computer vision, multilabel classification is typically implemented in a max-margin multilabel learning framework, where the inter-label separability is characterized by the sample-specific classification margins between labels. However, the conventional multilabel classification approaches are usually incapable of effectively exploring the intrinsic inter-label correlations as well as jointly modeling the interactions between inter-label correlations and multilabel classification. To address this issue, we propose a multilabel classification framework based on a joint learning approach called label graph learning (LGL) driven weighted Support Vector Machine (SVM). In principle, the joint learning approach explicitly models the inter-label correlations by LGL, which is jointly optimized with multilabel classification in a unified learning scheme. As a result, the learned label correlation graph well fits the multilabel classification task while effectively reflecting the underlying topological structures among labels. Moreover, the inter-label interactions are also influenced by label-specific sample communities (each community for the samples sharing a common label). Namely, if two labels have similar label-specific sample communities, they are likely to be correlated. Based on this observation, LGL is further regularized by the label Hypergraph Laplacian. Experimental results have demonstrated the effectiveness of our approach over several benchmark data sets.
Person Reidentification Based on Elastic Projections

Person reidentification usually refers to matching people in different camera views in nonoverlapping multicamera networks. Many existing methods learn a similarity measure by projecting the raw feature to a latent subspace to make the same target’s distance smaller than different targets’ distances. However, the same targets captured in different camera views should hold the same intrinsic attributes while different targets should hold different intrinsic attributes. Projecting all the data to the same subspace would cause loss of such an information and comparably poor discriminability. To address this problem, in this paper, a method based on elastic projections is proposed to learn a pairwise similarity measure for person reidentification. The proposed model learns two projections, positive projection and negative projection, which are both representative and discriminative. The representability refers to: for the same targets captured in two camera views, the positive projection can bridge the corresponding appearance variation and represent the intrinsic attributes of the same targets, while for the different targets captured in two camera views, the negative projection can explore and utilize the different attributes of different targets. The discriminability means that the intraclass distance should become smaller than its original distance after projection, while the interclass distance becomes larger on the contrary, which is the elastic property of the proposed model. In this case, prior information of the original data space is used to give guidance for the learning phase; more importantly, similar targets (but not the same) are effectively reduced by forcing the same targets to become more similar and different targets to become more distinct. The proposed model is evaluated on three benchmark data sets, including VIPeR, GRID, and CUHK, and achieves better performance than other methods.
Spatial-Aware Object-Level Saliency Prediction by Learning Graphlet Hierarchies

To fill the semantic gap between the predictive power of computational saliency models and human behavior, this paper proposes to predict where people look at using spatial-aware object-level cues. While object-level saliency has been recently suggested by psychophysics experiments and shown effective with a few computational models, the spatial relationship between the objects has not yet been explored in this context. We in this work for the first time explicitly model such spatial relationship, as well as leveraging semantic information of an image to enhance object-level saliency modeling. The core computational module is a graphlet-based (i.e., graphlets are moderate-sized connected subgraphs) deep architecture, which hierarchically learns a saliency map from raw image pixels to object-level graphlets (oGLs) and further to spatial-level graphlets (sGLs). Eye tracking data are also used to leverage human experience in saliency prediction. Experimental results demonstrate that the proposed oGLs and sGLs well capture object-level and spatial-level cues relating to saliency, and the resulting saliency model performs competitively compared with the state-of-the-art.
Nonnegative Discriminant Matrix Factorization

Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification. NDMF integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function. NDMF considers the incoherent information of both factors in standard NMF and is proposed to enhance the discriminant ability of the learned base matrix. NDMF projects the low-dimensional representation of the subspace of the base matrix to regularize the NMF for discriminant subspace learning. Based on the Euclidean distance metric and the generalized Kullback–Leibler (KL) divergence, two kinds of iterative algorithms are presented to solve the optimization problem. The between- and within-class scatter matrices are divided into positive and negative parts for the update rules and the proofs of the convergence are also presented. Extensive experimental results demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art discriminant NMF algorithms.
Effective video event detection via subspace projection

This paper describes a new video event detection framework based on subspace selection technique. With the approach, feature vectors presenting different kinds of video information can be easily projected from different modalities onto an unified subspace, on which recognition process can be performed. The approach is capable of discriminating different classes and preserving the intra-modal geometry of samples within an identical class. Distinguished from the existing multi-modal detection methods, the new system works well when some modalities are not available. Experimental results based on soccer video and TRECVID news video collections demonstrate the effectiveness, efficiency and robustness of the proposed method for individual recognition tasks in comparison to the existing approaches.
Iterative Subspace Analysis Based on Feature Line Distance

Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.
Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment

One of the primary goals of the airborne vehicle detection system is to reduce the risks of incident collisions and to relieve traffic jam caused by the increasing number of vehicles. Different from the stationary systems, which are usually fixed on buildings, the airborne systems in unmanned aircrafts or satellites take the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. The direct application of the traditional image processing techniques to the problem may result in low detection rate or cannot meet the requirements of real-time applications. To address these problems, a new and efficient method composed by two stages, attention focus extraction and vehicle classification is proposed in this paper. Our work makes two key contributions. The first is the introduction of a new attention focus extraction algorithm, which can quickly detect the candidate vehicle regions to make the algorithm focus on much smaller regions for faster computation. The second contribution is a simple and efficient classification process, which is built using the AdaBoost learning algorithm. The classification process, which is a hierarchical structure, is designed to obtain a lower false alarm rate by looking for vehicles in the candidate regions. Experimental results demonstrate that, compared with other representative algorithms, our method can obtain better performance in terms of higher detection rate and lower false positive rate, while meeting the needs of real-time application.
Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization

Fine-grained image categorization is a challenging task aiming at distinguishing objects belonging to the same basic-level category, e.g., leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and so on. Most of the existing methods either have difficulties to detect discriminative object components automatically, or suffer from the limited amount of training data in each sub-category. To solve these problems, this paper proposes a new fine-grained image categorization model. The key is a dense graph mining algorithm that hierarchically localizes discriminative object parts in each image. More specifically, to mimic the human hierarchical perception mechanism, a superpixel pyramid is generated for each image. Thereby, graphlets from each layer are constructed to seamlessly capture object components. Intuitively, graphlets representative to each super-/sub-category is densely distributed in their feature space. Thus, a dense graph mining algorithm is developed to discover graphlets representative to each super-/sub-category. Finally, the discovered graphlets from pairwise images are integrated into an image kernel for fine-grained recognition. Theoretically, the learned kernel can generalize several state-of-the-art image kernels. Experiments on nine image sets demonstrate the advantage of our method. Moreover, the discovered graphlets from each sub-category accurately capture those tiny discriminative object components, e.g., bird claws, heads, and bodies.
Multitraining Support Vector Machine for Image Retrieval

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively