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Jie Zhou - One of the best experts on this subject based on the ideXlab platform.
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simultaneous local Binary Feature learning and encoding for homogeneous and heterogeneous face recognition
2018Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which usually require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which automatically learns face representation from raw pixels. Unlike existing Binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact Binary face descriptor (CBFD) which use a two-stage Feature extraction procedure, our SLBFLE jointly learns Binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage Feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local Binary Feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogenous face matching. Unlike most existing coupled Feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets including the LFW, YouTube Face (YTF), FERET, PaSC, CASIA VIS-NIR 2.0, and Multi-PIE datasets are presented to demonstrate the effectiveness of the proposed methods.
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context aware local Binary Feature learning for face recognition
2018Co-Authors: Yueqi Duan, Jianjiang Feng, Jie ZhouAbstract:In this paper, we propose a context-aware local Binary Feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact Binary face descriptor (CBFD) which learn each Feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different Binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware Binary vector. Then, we perform clustering on the learned Binary codes to construct a codebook, and extract a histogram Feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local Binary multi-scale Feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the Feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.
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learning rotation invariant local Binary descriptor
2017Co-Authors: Yueqi Duan, Jianjiang Feng, Jie ZhouAbstract:In this paper, we propose a rotation-invariant local Binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local Binary descriptors, such as local Binary pattern and its variants, which require strong prior knowledge, local Binary Feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local Binary descriptors, such as compact Binary face descriptor and simultaneous local Binary Feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational Binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same Binary descriptor. Then, we construct a codebook by a clustering method on the learned Binary codes, and obtain a histogram Feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local Binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence Binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.
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simultaneous local Binary Feature learning and encoding for face recognition
2015Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which is automatically learned from raw pixels. Unlike existing Binary face descriptors such as the LBP and discriminant face descriptor (DFD) which use a two-stage Feature extraction approach, our SLBFLE jointly learns Binary codes for local face patches and the codebook for Feature encoding so that discriminative information from raw pixels can be simultaneously learned with a one-stage procedure. Experimental results on four widely used face datasets including LFW, YouTube Face (YTF), FERET and PaSC clearly demonstrate the effectiveness of the proposed method.
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cost sensitive local Binary Feature learning for facial age estimation
2015Co-Authors: Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a cost-sensitive local Binary Feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for Feature representation, our CS-LBFL method learns discriminative local Features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local Binary Features are more robust to illumination and expression variations than holistic Features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional Binary codes, where Binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local Binary codes within each face image as a real-valued histogram Feature for face representation. Moreover, we propose a cost-sensitive local Binary multi-Feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets.
Venice Erin Liong - One of the best experts on this subject based on the ideXlab platform.
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simultaneous local Binary Feature learning and encoding for homogeneous and heterogeneous face recognition
2018Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which usually require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which automatically learns face representation from raw pixels. Unlike existing Binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact Binary face descriptor (CBFD) which use a two-stage Feature extraction procedure, our SLBFLE jointly learns Binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage Feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local Binary Feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogenous face matching. Unlike most existing coupled Feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets including the LFW, YouTube Face (YTF), FERET, PaSC, CASIA VIS-NIR 2.0, and Multi-PIE datasets are presented to demonstrate the effectiveness of the proposed methods.
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simultaneous local Binary Feature learning and encoding for face recognition
2015Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which is automatically learned from raw pixels. Unlike existing Binary face descriptors such as the LBP and discriminant face descriptor (DFD) which use a two-stage Feature extraction approach, our SLBFLE jointly learns Binary codes for local face patches and the codebook for Feature encoding so that discriminative information from raw pixels can be simultaneously learned with a one-stage procedure. Experimental results on four widely used face datasets including LFW, YouTube Face (YTF), FERET and PaSC clearly demonstrate the effectiveness of the proposed method.
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cost sensitive local Binary Feature learning for facial age estimation
2015Co-Authors: Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a cost-sensitive local Binary Feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for Feature representation, our CS-LBFL method learns discriminative local Features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local Binary Features are more robust to illumination and expression variations than holistic Features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional Binary codes, where Binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local Binary codes within each face image as a real-valued histogram Feature for face representation. Moreover, we propose a cost-sensitive local Binary multi-Feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets.
Hu Zhu - One of the best experts on this subject based on the ideXlab platform.
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Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks
2018Co-Authors: Yansheng Li, Yongjun Zhang, Xin Huang, Hu ZhuAbstract:As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted Features as inputs and map the high-dimensional Feature vector to the low-dimensional Binary Feature vector to reduce Feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep Feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of Feature descriptors, we can automatically learn good Feature extraction operations and Feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.
Jiwen Lu - One of the best experts on this subject based on the ideXlab platform.
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simultaneous local Binary Feature learning and encoding for homogeneous and heterogeneous face recognition
2018Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which usually require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which automatically learns face representation from raw pixels. Unlike existing Binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact Binary face descriptor (CBFD) which use a two-stage Feature extraction procedure, our SLBFLE jointly learns Binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage Feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local Binary Feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogenous face matching. Unlike most existing coupled Feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets including the LFW, YouTube Face (YTF), FERET, PaSC, CASIA VIS-NIR 2.0, and Multi-PIE datasets are presented to demonstrate the effectiveness of the proposed methods.
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simultaneous local Binary Feature learning and encoding for face recognition
2015Co-Authors: Jiwen Lu, Venice Erin Liong, Jie ZhouAbstract:In this paper, we propose a simultaneous local Binary Feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local Binary pattern (LBP) and Gabor Features which require strong prior knowledge, our SLBFLE is an unsupervised Feature learning approach which is automatically learned from raw pixels. Unlike existing Binary face descriptors such as the LBP and discriminant face descriptor (DFD) which use a two-stage Feature extraction approach, our SLBFLE jointly learns Binary codes for local face patches and the codebook for Feature encoding so that discriminative information from raw pixels can be simultaneously learned with a one-stage procedure. Experimental results on four widely used face datasets including LFW, YouTube Face (YTF), FERET and PaSC clearly demonstrate the effectiveness of the proposed method.
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
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hierarchical encoding of Binary descriptors for image matching
2015Co-Authors: Lingling Tong, Qi TianAbstract:Binary descriptors are increasingly popular such as BRIEF, ORB, and BRISK. Typically, Binary descriptors are computed by comparing pairs of image pixel intensities over a sampling pattern. To improve matching performance, lots of progresses have been made on the selection of pixel pairs, yet the discriminative power of pixel pairs is not fully studied. In this paper, we hierarchically encode the intensity differences between pixel pairs to build our discriminative Binary Feature. To avoid efficiency loss, a stepwise distance metric is proposed for directly matching these descriptors without decoding. Extensive experiments on benchmark database demonstrate that we outperform the state-of-the-art Binary descriptors in image matching performance. In addition, we show that our encoded descriptor is as efficient as the state-of-the-art Binary descriptors in both computing and matching.
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Binary Feature from intensity quantization and weakly spatial contextual coding for image search
2015Co-Authors: Dongye Zhuang, Dongming Zhang, Qi TianAbstract:During the past few years, a number of local Binary Feature descriptors for images have been proposed, e.g. BRIEF, ORB, BRISK, and FREAK. The Binary descriptors have several advantages over the well-established floating vector descriptors such as SIFT and SURF, for their fast computing speed and much low memory consumption. Nevertheless, the Binary descriptor still suffer from the poor performance in computer vision applications for its low discriminative power and Hamming distance metric. To improve the capability of Binary descriptors, some works focusing on the points selection pattern algorithm in the descriptor extraction are proposed. These works mostly adopt more optimal selection pattern (BRISK and FREAK) to enhance the performance of Binary descriptors, rather than random chosen pattern. In our study, however, the points selection algorithm does not have much contributions for the promotion on performance. Therefore, in this paper, we try to solve the problem of low discriminative power and robustness through two novel methods: Intensity Difference Quantization and Weakly Spatial Context Coding. The experimental results on the public datasets show that our method can significantly boost the performance of Binary Features and highly enhance the retrieval accuracy of the image search system, even though our proposed method increases slightly memory usage and computing efficiency, compared to the original Binary descriptors.
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improved Binary Feature matching through fusion of hamming distance and fragile bit weight
2013Co-Authors: Dongye Zhuang, Dongming Zhang, Qi TianAbstract:In this paper, we define a new metric, the Fragile Bit Weight (FBW), which is used in Binary Feature matching and measures how two Features differ. High FBWs are associated with genuine matches between two Binary Features and low FBWs are associated with impostor ones. One bit in Binary Feature is deemed fragile if its sign of value reverses easily across the local image patch that has changed slightly. Previous Binary Feature extract algorithms ignore the fact that the signs of fragile bits are not stable through image transform. Rather than ignore fragile bits completely, we consider what beneficial information can be obtained from those fragile bits. In our approach, we exploit FBW as a measure in Binary Feature match to remove the false matches. In experiments, using FBW can effectively remove the false matches and highly improve the accuracy of Feature match. Then, we find that fusion of FBW and Hamming distance work better in Feature matching than Hamming distance alone. Furthermore, FBW can easily integrate in the well-established Binary Feature schemes if those descriptor bit in extract from comparison of pixels.