TBA
A scientific understanding of emotion experience requires information on the contexts in which the emotion is induced. Moreover, as one of the primary functions of music is to regulate the listener's mood, the individual's short-term music preference may reveal the emotional state of the individual. In light of these observations, this paper presents the first scientific study that exploits the online repository of social data to investigate the connections between a blogger's emotional state, user context manifested in the blog articles, and the content of the music titles the blogger attached to the post. A number of computational models are developed to evaluate the accuracy of different content or context cues in predicting emotional state, using 40,000 pieces of music listening records collected from the social blogging website LiveJournal. Our study shows that it is feasible to computationally model the latent structure underlying music listening and mood regulation. The average area under the receiver operating characteristic curve (AUC) for the content-based and context-based models attains 0.5462 and 0.6851, respectively. The association among user mood, music emotion, and individual's personality is also identified.
In this paper, we address the problem of robust face recognition using single sample per person. Given only one training image per subject of interest, our proposed method is able to recognize query images with illumination or expression changes, or even the corrupted ones due to occlusion. In order to model the above intra-class variations, we advo- cate the use of external data (i.e., images of subjects not of interest) for learning an exemplar-based dictionary. This dictionary provides auxiliary yet representative information for handling intra-class variation, while the gallery set con- taining one training image per class preserves separation between dierent subjects for recognition purposes. Our ex- periments on two face datasets conrm the eectiveness and robustness of our approach, which is shown to outperform state-of-the-art sparse representation based methods.
In physical-layer security, secret bits are extracted from wireless channels. With the assumption of channel reciprocity, the legitimate users share the same channel which is independent of the channels between the legitimate users and the eavesdropper, leading to secure transmissions. However, practical implementation of the physical layer security faces many challenges. First, for the correlated channel such as the multiple-input and multiple-output (MIMO) channel, the security is decreased due to the correlation between the generated secret bits. Second, the nearby eavesdropper posts a security threat due to observing the same channel as the legitimate user's. Third, the eavesdroppers might try to reconstruct the wireless environments. In this paper, we propose two practical physical layer security schemes for the MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems: the precoding matrix index (PMI)-based secret key generation with rotation matrix (MOPRO) and the channel quantization-based (MOCHA) scheme. The former utilizes PMI and rotated reference signals to prevent the eavesdroppers from learning the secret key information and the latter applies channel quantization in order to extract more secret key bits. It is shown that not only the secure communication but also the MIMO gain can be guaranteed by using the proposed schemes.
It has been a challenging task to estimate optical flow for videos in which either foreground or background exhibits remarkable motion information (i.e., large displacement), or those with insufficient resolution due to artifacts like motion blur or noise. We present a novel optical flow algorithm, which approaches the above problem as solving the task of energy minimization, which exploits image data and smoothness terms at the superpixel level. Our proposed method can be considered as an extended mean-shift algorithm, which advances color and gradient information of superpixels across consecutive frames with smoothness guarantees. Since we do not require assumptions of linearlization during optimization (as standard optical flow approaches do), we are able to alleviate local minimum problems and thus produce improved estimation results. Empirical results on the MPI-Sintel video dataset verify the effectiveness of our proposed method.
Cross-view action recognition is a challenging problem, since one typically does not have sufficient training data at the target view of interest. With recent developments of domain adaptation, we propose a novel low-rank based domain adaptation model for mapping labeled data from the original source view to the target view, so that training and testing can be performed at that domain. Our model not only provides an effective way for associating image data across different domains, we further advocate the structural incoherence between transformed data of different categories. As a result, additional data discriminating ability is introduced to our domain adaptation model, and thus improved recognition can be expected. Experimental results on the IXMAS dataset verify the effectiveness of our proposed method, which is shown to outperform state-of-the-art domain adaptation approaches.
Techniques of domain adaptation have been applied to address cross-domain recognition problems. In particular, such techniques favor the scenarios in which labeled data can be obtained at the source domain, but only few labeled target domain data are available during the training stage. In this paper, we propose a domain adaptation approach which is able to transfer source domain labeled data to the target domain, so that one can collect a sufficient amount of training data at that domain for recognition purposes. By advancing low-rank matrix decomposition for obtaining representative cross-domain data, our proposed model aims at transferring source domain labeled data to the target domain while preserving class label information. This introduces additional discriminating ability into our model, and thus improved recognition can be expected. Empirical results on cross-domain image datasets confirm the use of our proposed model for solving cross-domain recognition problems.
FingerPad: Private and Subtle Interaction Under Fignertips
This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory results.
Anomaly detection has been an important research topic in data mining and machine learning. Many real-world applications such as intrusion or credit card fraud detection require an effective and efficient framework to identify deviated data instances. However, most anomaly detection methods are typically implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing computation and memory requirements. In this paper, we propose an online oversampling principal component analysis (osPCA) algorithm to address this problem, and we aim at detecting the presence of outliers from a large amount of data via an online updating technique. Unlike prior principal component analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our approach is especially of interest in online or large-scale problems. By oversampling the target instance and extracting the principal direction of the data, the proposed osPCA allows us to determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector. Since our osPCA need not perform eigen analysis explicitly, the proposed framework is favored for online applications which have computation or memory limitations. Compared with the well-known power method for PCA and other popular anomaly detection algorithms, our experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency.