Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric ...factors in both bounding-box regression and nonmaximum suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider three geometric factors, that is: 1) overlap area; 2) normalized central-point distance; and 3) aspect ratio, which are crucial for measuring bounding-box regression in object detection and instance segmentation. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted <inline-formula> <tex-math notation="LaTeX">\ell _{n} </tex-math></inline-formula>-norm loss and IoU-based loss. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires fewer iterations. Cluster-NMS is very efficient due to its pure GPU implementation, and geometric factors can be incorporated to improve both AP and AR. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT and BlendMask-RT), and object detection (e.g., YOLO v3, SSD, and Faster R-CNN) models. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR 100 for object detection, and +1.1 AP and +3.5 AR 100 for instance segmentation, with 27.1 FPS on one NVIDIA GTX 1080Ti GPU. All the source code and trained models are available at https://github.com/Zzh-tju/CIoU .
The concept of dependency in a neighborhood rough set model is an important evaluation function for the feature selection. This function considers only the classification information contained in the ...lower approximation of the decision while ignoring the upper approximation. In this paper, we construct a class of uncertainty measures: decision self-information for the feature selection. These measures take into account the uncertainty information in the lower and the upper approximations. The relationships between these measures and their properties are discussed in detail. It is proven that the fourth measure, called relative neighborhood self-information, is better for feature selection than the other measures, because not only does it consider both the lower and the upper approximations but also the change of its magnitude is largest with the variation of feature subsets. This helps to facilitate the selection of optimal feature subsets. Finally, a greedy algorithm for feature selection has been designed and a series of numerical experiments was carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm often chooses fewer features and improves the classification accuracy in most cases.
Due to complex semantics, a sample may be associated with multiple labels in various classification and recognition tasks. Multilabel learning generates training models to map feature vectors to ...multiple labels. There are several significant challenges in multilabel learning. Samples in multilabel learning are usually described with high-dimensional features and some features may be sequentially extracted. Thus, we do not know the full feature set at the beginning of learning, referred to as streaming features. In this paper, we introduce fuzzy mutual information to evaluate the quality of features in multilabel learning, and design efficient algorithms to conduct multilabel feature selection when the feature space is completely known or partially known in advance. These algorithms are called multilabel feature selection with label correlation (MUCO) and multilabel streaming feature selection (MSFS), respectively. MSFS consists of two key steps: online relevance analysis and online redundancy analysis. In addition, we design a metric to measure the correlation between the label sets, and both MUCO and MSFS take label correlation to consideration. The proposed algorithms are not only able to select features from streaming features, but also able to select features for ordinal multilabel learning. However streaming feature selection is more efficient. The proposed algorithms are tested with a collection of multilabel learning tasks. The experimental results illustrate the effectiveness of the proposed algorithms.
A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a ...maximal dependency function. It does not fit a given dataset well and cannot ideally describe the differences in sample classification. Therefore, in this study, we introduce a new model for handling this problem. First, we define the fuzzy decision of a sample using the concept of fuzzy neighborhood. Then, a parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced. This can guarantee that the membership degree of a sample to its own category reaches the maximal value. Furthermore, this approach can fit a given dataset and effectively prevents samples from being misclassified. Finally, we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection. Twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms, and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets, especially for those datasets for which different categories exhibit a large degree of overlap.
Latent Multi-view Subspace Clustering Changqing Zhang; Qinghua Hu; Huazhu Fu ...
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2017-July
Conference Proceeding
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary ...information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. The proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing with homogeneous feature ...selection, namely, numerical or categorical features. In this paper, we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection. As the classical rough set model can just be used to evaluate categorical features, we generalize this model with neighborhood relations and introduce a neighborhood rough set model. The proposed model will degrade to the classical one if we specify the size of neighborhood zero. The neighborhood model is used to reduce numerical and categorical features by assigning different thresholds for different kinds of attributes. In this model the sizes of the neighborhood lower and upper approximations of decisions reflect the discriminating capability of feature subsets. The size of lower approximation is computed as the dependency between decision and condition attributes. We use the neighborhood dependency to evaluate the significance of a subset of heterogeneous features and construct forward feature subset selection algorithms. The proposed algorithms are compared with some classical techniques. Experimental results show that the neighborhood model based method is more flexible to deal with heterogeneous data.
Given a group photograph, it is interesting and useful to judge whether the characters in it share specific kinship relation, such as father-daughter, father-son, mother-daughter, or mother-son. ...Recently, facial image-based kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods.
Heterogeneous feature representations are widely used in machine learning and pattern recognition, especially for multimedia analysis. The multi-modal, often also high- dimensional , features may ...contain redundant and irrelevant information that can deteriorate the performance of modeling in classification. It is a challenging problem to select the informative features for a given task from the redundant and heterogeneous feature groups. In this paper, we propose a novel framework to address this problem. This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO. Given diverse groups of discriminative features, the proposed technique first converts the multi-modal data into a unified representation with different branches of the multi-modal deep neural networks. Then, through solving a sparse group LASSO problem, the feature selection component is used to derive a weight vector to indicate the importance of the feature groups. Finally, the feature groups with large weights are considered more relevant and hence are selected. We evaluate our framework on three image classification datasets. Experimental results show that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.
Dimensionality reduction aims to map the high-dimensional inputs onto a low-dimensional subspace, in which the similar points are close to each other and vice versa. In this paper, we focus on ...unsupervised dimensionality reduction for the data with multiple views, and propose a novel method, called Multi-view Dimensionality co-Reduction. Our method flexibly exploits the complementarity of multiple views during the dimensionality reduction and respects the similarity relationships between data points across these different views. The kernel matching constraint based on Hilbert-Schmidt Independence Criterion enhances the correlations and penalizes the disagreement of different views. Specifically, our method explores the correlations within each view independently, and maximizes the dependence among different views with kernel matching jointly. Thus, the locality within each view and the consistence between different views are guaranteed in the subspaces corresponding to different views. More importantly, benefiting from the kernel matching, our method need not depend on a common low-dimensional subspace, which is critical to reduce the influence of the unbalanced dimensionalities of multiple views. Specifically, our method explicitly produces individual low-dimensional projections for individual views, which could be applied for new coming data in the out-of-sample manner. Experiments on both clustering and recognition tasks demonstrate the advantages of the proposed method over the state-of-the-art approaches.
Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle ...partially labeled data. So far, few studies on attribute selection in partially labeled data have been conducted. In this paper, the concept of discernibility pair based on rough set theory is raised to construct a uniform measure for the attributes in both supervised framework and unsupervised framework. Based on discernibility pair, two kinds of semisupervised attribute selection algorithm based on rough set theory are developed to handle partially labeled categorical data. Experiments demonstrate the effectiveness of the proposed attribute selection algorithms.
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