In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fu...In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.展开更多
A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples ...A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.展开更多
Object recognition has many applications in human-machine interaction and multimedia retrieval.However,due to large intra-class variability and inter-class similarity,accurate recognition relying only on RGB data is s...Object recognition has many applications in human-machine interaction and multimedia retrieval.However,due to large intra-class variability and inter-class similarity,accurate recognition relying only on RGB data is still a big challenge.Recently,with the emergence of inexpensive RGB-D devices,this challenge can be better addressed by leveraging additional depth information.A very special yet important case of object recognition is hand-held object recognition,as manipulating objects with hands is common and intuitive in human-human and human-machine interactions.In this paper,we study this problem and introduce an effective framework to address it.This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information.In the object recognition stage,this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy.In particular,we incorporate handcrafted and deep learned features and study several multi-step fusion variants.Experimental evaluations validate the effectiveness of the proposed method.展开更多
The recent publication by Mollaoglu et al.1 in Cell reveals an unexpected role for tumor derived IL4 in driving immunotherapy resistance in ovarian cancer(OvCa).This finding nominates the combination of immunotherapy ...The recent publication by Mollaoglu et al.1 in Cell reveals an unexpected role for tumor derived IL4 in driving immunotherapy resistance in ovarian cancer(OvCa).This finding nominates the combination of immunotherapy and IL4-signaling targeting strategies as a promising new approach for the treatment of advanced OvCa.Ovarian Cancer(OvCa)is the third most common gynecological malignant disease affecting women.2 It is often diagnosed at late stages and is characterized by heterogenous features with limited treatment options.Initial response to standard of care platinumbased chemotherapy combined with surgery is often followed by disease relapse and subsequent death of patients.Despite the recent success of immune checkpoint inhibition in different cancer entities,most OvCa patients do not benefit from immunotherapy-based treatment approaches.The responsiveness of ovarian tumors to immune checkpoint blockade(ICB)is thereby hindered by weak immunogenicity due to low mutational burden and an immune suppressive tumor microenvironment(TME)characterized by heterogenous immune cell infiltration.3 Still,functional evidence for key factors that govern cancer cellimmune cell interaction and drive immunotherapy resistance in OvCa remains limited.展开更多
基金supported by National Natural Science Foundation of China(Nos.61271432 and 61333016)
文摘In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.
基金The National Natural Science Foundation of China(No.61305058)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.12KJB520003)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20130471)the Scientific Research Foundation for Advanced Talents by Jiangsu University(No.13JDG093)
文摘A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.
基金This work was supported in part by the National Basic Research 973 Program of China under Grant No.2012CB316400the National Natural Science Foundation of China under Grant Nos.61322212 and 61450110446+1 种基金the National High Technology Research and Development 863 Program of China under Grant No.2014AA015202the Chinese Academy of Sciences Fellowships for Young International Scientists under Grant No.2011Y1GB05.This work is also funded by Lenovo Outstanding Young Scientists Program(LOYS).
文摘Object recognition has many applications in human-machine interaction and multimedia retrieval.However,due to large intra-class variability and inter-class similarity,accurate recognition relying only on RGB data is still a big challenge.Recently,with the emergence of inexpensive RGB-D devices,this challenge can be better addressed by leveraging additional depth information.A very special yet important case of object recognition is hand-held object recognition,as manipulating objects with hands is common and intuitive in human-human and human-machine interactions.In this paper,we study this problem and introduce an effective framework to address it.This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information.In the object recognition stage,this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy.In particular,we incorporate handcrafted and deep learned features and study several multi-step fusion variants.Experimental evaluations validate the effectiveness of the proposed method.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation,LE 3613/3-1).
文摘The recent publication by Mollaoglu et al.1 in Cell reveals an unexpected role for tumor derived IL4 in driving immunotherapy resistance in ovarian cancer(OvCa).This finding nominates the combination of immunotherapy and IL4-signaling targeting strategies as a promising new approach for the treatment of advanced OvCa.Ovarian Cancer(OvCa)is the third most common gynecological malignant disease affecting women.2 It is often diagnosed at late stages and is characterized by heterogenous features with limited treatment options.Initial response to standard of care platinumbased chemotherapy combined with surgery is often followed by disease relapse and subsequent death of patients.Despite the recent success of immune checkpoint inhibition in different cancer entities,most OvCa patients do not benefit from immunotherapy-based treatment approaches.The responsiveness of ovarian tumors to immune checkpoint blockade(ICB)is thereby hindered by weak immunogenicity due to low mutational burden and an immune suppressive tumor microenvironment(TME)characterized by heterogenous immune cell infiltration.3 Still,functional evidence for key factors that govern cancer cellimmune cell interaction and drive immunotherapy resistance in OvCa remains limited.
