Many kinds of efficient greedy subspace clustering methods have been proposed to cut down the computation time in clustering large-scale multimedia datasets. However, these methods are easy to fall into local optimum ...
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Learning based hashing is gaining traction in large-scale retrieval systems. It aims to learn compact binary codes that can preserve semantic similarity in the hamming space. This paper presents a supervised topology ...
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Multi-view clustering aims to cluster data with multiple sources of information. Comparing with single view clustering, it is challenging to make use of the extra information embedded in multiple views. This paper pre...
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In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt m...
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ISBN:
(纸本)9781467383929
In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt model degenerate in target domain, i.e., the test domain. To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE). The proposed BAE attempts to shift source domain samples to target domain, and also shift the target domain samples to source domain. The non-linear transformation of BAE ensures the feasibility of shifting between domains, and the distribution consistency between the shifted domain and the desirable domain is constrained by sparse reconstruction between them. As a result, the shifted source domain is supervised and follows similar distribution as target domain. Therefore, any supervised method can be applied on the shifted source domain to train a classifier for classification in target domain. The proposed method is evaluated on three domain adaptation scenarios of face recognition, i.e., domain adaptation across view angle, ethnicity, and imaging sensor, and the promising results demonstrate that our proposed BAE can shift samples between domains and thus effectively deal with the domain discrepancy.
This paper presents a writing-in-the-air fingertip positioning system using a camera as an input to a computer. The frame difference method is employed to extract the finger movement area and the vertical gradient is ...
Many complex behaviors that do not require learning are displayed and are termed innate. Although traditionally the subject matter of ethology, innate behaviors offer a unique entry point for neuroscientists to dissec...
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Many complex behaviors that do not require learning are displayed and are termed innate. Although traditionally the subject matter of ethology, innate behaviors offer a unique entry point for neuroscientists to dissect the physiological mechanisms governing complex behaviors. Since the last century, converging evidence has implicated the hypothalamus as the central brain area that controls innate behaviors. Recent studies using cutting-edge tools have revealed that genetically-defined populations of neurons residing in distinct hypothalamic nuclei and their associated neural pathways regulate the initiation and maintenance of diverse behaviors including feeding, sleep, aggression, and parental care. Here, we review the newly-defined hypothalamic pathways that regulate each innate behavior. In addition, emerging general principles of the neural control of complex behaviors are discussed.
Objective: A novel dry electrode is developed to improve comfortability and the capability of alleviating motion interference by combining micro-needles array(MNA)with flexible ***: Silicon MNA with sharp tips and lim...
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Objective: A novel dry electrode is developed to improve comfortability and the capability of alleviating motion interference by combining micro-needles array(MNA)with flexible ***: Silicon MNA with sharp tips and limited height is fabricated and transferred on a flexible Poly-dimethylsiloxane(PDMS)substrate through ***(3,4-ethylenedioxythiophene)doped with poly(styrenesulfonate)(PEDOT/PSS)is coated on the surface of flexible MNA to form a conductive ***: Flexible dry electrode with 1.2 centimeter diameter is successfully *** mean impedance magnitudes(measured on skin)at 10 Hz is 61.2 ± 31.3 kΩ·cm2 for flexible dry electrode,while the values are 114.9 ± 36.1 kΩ·cm2 for wet electrode and 335.7 ± 110.5 kΩ·cm2 for flexible planar dry electrode *** the process of biopotential recording,the flexible dry electrode has the similar performance as that of wet *** exhibits more stable recording stability than rigid dry electrode in the movement ***: By integrating flexible PDMS substrate,sharp and hard MNA structure,as well as PEDOT/PSS coated surface together,a novel dry electrode is developed to meet the comfortable and anti-motion interference requirement of wearable ***: The novel flexible dry electrode provides a simple and comfortable method to record biopotential signals in daily life.
Multimedia content is increasingly available in multiple modalities. Each modality provides a different representation of the same entity. This paper studies the problem of joint representation of the text and image c...
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ISBN:
(纸本)9781479961016
Multimedia content is increasingly available in multiple modalities. Each modality provides a different representation of the same entity. This paper studies the problem of joint representation of the text and image components of multimedia documents. However, most existing algorithms focus more on inter-modal connection rather than intramodal feature extraction. In this paper, a simple yet effective principal affinity representation (PAR) approach is proposed to exploit the affinity representations of different modalities with local cluster samples. Afterwards, multi-class logistic regression model is adopted to learn the projections from principal affinity representation to semantic labels vectors. Inner product distance is further used to improve cross-modal retrieval performance. Extensive experiments on three benchmark datasets illustrate that our proposed method obtains significant improvements over the state-of-the-art subspace learning based cross-modal methods.
3D Reconstruction from the microscopy images of serial sections plays an important role in analysis structure of biological specimens, such as neuronal circuits in brain tissue. During specimen sectioning and collecti...
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3D Reconstruction from the microscopy images of serial sections plays an important role in analysis structure of biological specimens, such as neuronal circuits in brain tissue. During specimen sectioning and collecting, it is very hard to prevent wrinkle from these sections, which introduce distortion when imaging in electron microscopy and cause failure of structure reconstruction. In this paper, we propose a pipeline for registration of serial sections with wrinkle. First, Scale Invariant Feature Transform (SIFT) is used to detect corresponding landmarks across adjacent sections. Second, wrinkle areas are labeled manually in microscopy images, which is easy to distinguish by eye. Finally, a modified Moving-Least-Square deformation algorithm is applied to register adjacent sections with wrinkle. The algorithm reflects the discontinuity around wrinkle areas while keeps the smoothness in other regions. Experimental results demonstrate the effectiveness of our method.
There has been significant progress in object part localization such as human pose estimation and facial landmark detection. In most of the previous methods, two phenomena are ignored. Firstly, they usually output a s...
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ISBN:
(纸本)9781479961016
There has been significant progress in object part localization such as human pose estimation and facial landmark detection. In most of the previous methods, two phenomena are ignored. Firstly, they usually output a set of candidate pose hypotheses but the hypothesis with the highest score obtained by Non-Maximum Suppression (NMS) is not always the optimal result. Secondly, they can not get exactly bilaterally symmetric keypoints on the mirrored images even though the training data is always augmented with mirrored images. In fact, the intrinsic relationship between the original image and the mirrored one is helpful for object part localization. In this paper, we propose Mirrored Non-Maximum Suppression (Mirrored NMS) which can utilize mirrored detections to improve the accuracy of object part localization. Experimental results show that our method can improve the state-of-the-art accuracy by 1.3~3.0% in PCP for human pose estimation and can produce more accurate results than averaging multiple hypotheses for facial landmark detection.
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