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With the rapid expansion of data scale, big data mining and analysis have attracted increasing attention. Outlier detection as an important task of data mining is widely used in many applications. However, conventional outlier detection methods have difficulty handling large-scale datasets. In addition, most of them typically can only identify global outliers and are over sensitive to parameters variation...
Due to the wide variety of copy videos, the existing video copy detection methods using single feature face great challenges, especially for video content matching, which are difficult to deal with various copy video transformations. To overcome this problem, a video copy detection method based on sparse representation of MPEG-2 spatial and temporal features is proposed in this paper. Firstly, the...
Due to the significant growth of video data over the Internet, it has become a popular choice for data hiding field. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic methods. In this paper, we propose...
This work analyzes excitation source to characterize glottal stops using integrated linear prediction (ILP) residual, derived by pitch-synchronous (PS) approach. The glottal stop consonant is produced due to laryngeal gesture in the form of constricted glottis. This pressed glottal configuration, leads to period to period irregularities, aperiodicity, and asymmetry. Normalized crosscorrelation coefficient...
Existing robust reversible watermarking methods usually have poor visual quality or unstable robustness and reversibility, implies that reversibility cannot be assured even in lossless channel. In this paper, a novel robust reversible watermarking method is proposed. In the proposed method, bit plane manipulation is applied to hide watermark bits in bit planes that are lesser affected by attacks....
General voice based access control systems are based on voice biometrics. This process enables an unauthorized access by recording the voice of the authorized person. So there is a requirement to prevent unauthorized access through recording speech. Other than voice biometrics, here we have two challenges. (i) To extract the authentication information. (ii) To find the unauthorized source. The speech...
Recently the use of QR code for data coding becomes significantly increasing especially for coding identity, health and other specific data. Since some types of data such as identity, health, etc is private data then it needs to be further authenticated. Furthermore, the owner of the identity should be authenticated as well. Therefore, we have to insert biometric features which will be further authenticated...
In this paper a robust audio steganography method is introduced which involves multiple layers for embedding secret data. In LSB technique generally a single or multiple bits are embedded always in one or some particular bit positions. So it is easy to get that data by knowing those positions. The robustness increases in LSB approach by considering higher LSB layer but it reduces the perceptibility...
The size of fraudulent activity is increasing rapidly, with individuals and organisations being at great risk. This paper inspects and determines the various components required to deliver a successful fraud detection system. It is hoped that in reading this report, the reader will comprehend what is required and see the true benefit of implementing such a solution. Following the structure of a robust...
Person re-identification plays a key role in video monitoring. Aiming for current person re-identifications for numerical complexity and extraction difficulty, we propose a simple and fast multi-feature. On the basis of analysis of difference excitation and orientation of Weber Local Descriptors, showing graphic texture features by difference excitation of circular field, showing graphic edge orientation...
This paper proposes an audio watermarking scheme based on singular-spectrum analysis (SSA) and differential evolution. In our framework, a watermark is embedded into an audio signal by modifying the amplitude of some oscillatory components which are decomposed by SSA, and a parameter set for the modification is determined by differential evolution. Test results showed that, although there is a trade-off...
In this work we present a novel method that generates compact semantic models for inferring human coordinated activities, including tasks that require the understanding of dual arms sequencing. These models are robust and invariant to observation from different executions styles of the same activity. Additionally, the obtained semantic representations are able to re-use the acquired knowledge to infer...
In this paper, we propose a L1-Norm driven Semi-Supervised Local Discriminant Projection (S2LDP-L1) for robust dimensionality reduction and image representation. For feature learning, our S2LDP-L1 approach aims at compacting local within-class divergence and separating local betweenclass divergence at the same time in addition to possessing the locality preserving power over all training data. To...
Sequential data is generated in many domains of science and technology. Although many studies have been carried out for sequence classification in the past decade, the problem is still a challenge, particularly for pattern-based methods. We identify two important issues related to pattern-based sequence classification which motivate the present work: the curse of parameter tuning and the instability...
In classification tasks, labeled data is a necessity but sometimes difficult or expensive to obtain. On the contrary, unlabeled data is usually abundant. Recently, different active learning algorithms are proposed to alleviate this issue by selecting the most informative data points to label. One family of active learning methods comes from Optimum Experimental Design (OED) in statistics. Instead...
Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best learning models including the state-of-the-art deep learning models. Recent attempts...
Shopping experience is important for both citizens and tourists. We present IntelligShop, a novel location-based augmented reality application that supports intelligent shopping experience in malls. As the key functionality, IntelligShop provides an augmented reality interface -- people can simply use ubiquitous smartphones to face mall retailers, then IntelligShop will automatically recognize the...
Machine learning and data mining have become ubiquitous tools in modern computing applications and large enterprise systems benefit from its adaptability and intelligent ability to infer patterns that can be used for prediction or decision-making. Great success has been achieved by applying machine learning and data mining to the security settings for large dataset, such as in intrusion detection,...
With the rapid expansion of data scale, big datamining and analysis has attracted increasing attention. Outlierdetection as an important task of data mining is widely usedin many applications. However, conventional outlier detectionmethods have difficulty handling large-scale datasets. In addition, most of them typically can only identify global outliersand are over sensitive to parameters variation...
The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global...
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