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There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags in this study. Our proposed methodology proved that CLOPE can also be used in a...
Saliency detection aims to focus attention on the important parts of a map, which is an excellent ability of human visual system. In this paper, we present a saliency detection model based on the principle that the pixels belong to the background are more disperse than the ones of the target area. Color contrast in different channels is employed to classify the pixels. Our method outperformed five...
Image segmentation is a basic task in image analysis and understanding and feature extraction is important but difficult. In this paper, we propose an effective feature selection method for color image segmentation which selects a group of mixed color features or channels from some different color spaces according to the principle of the least entropy of pixels frequency histogram distribution. Actually,...
Image based indoor localization is an important problem with many useful application. This paper proposes an indoor localization system for performing fine localization and less latency with more priori information, including tile angel and the relative height between camera optical center and origin in reference coordinate system (RCS). The system is divided into two stages: offline stage and online...
Visual-based indoor localization have become a favored research area in recent years. It can be used inside a building where GPS signals are often not available. And due to its low deployment cost, visual-based indoor localization has been implemented in the complicated indoor environment. However, in order to increase the accuracy of indoor localization, the scale of image database should be as large...
As some public buildings have become large in spatial scale, people find it more and more difficult to know their actual location in these buildings. Generally in the indoor environment, to get location information is relatively more complex than that in the outdoor environment, for traditional outdoor localization methods do not perform well in indoor environment. Under this circumstances, image...
Many researchers have demonstrated the good performance of spoofing detection systems under clean training and testing conditions. However, it is well known that the performance of speaker and speech recognition systems significantly degrades in noisy conditions. Therefore, it is of great interest to investigate the effect of noise on the performance of spoofing detection systems. In this paper, we...
Head pose classification and estimation are essential for many face detection and recognition tasks and tracking applications. This paper proposes the robust and fast algorithms for head pose classification from labeled head pose database by employing deep neural networks (DNNs). The DNNs have capabilities to learn from raw images and process large-scale training image datasets. The proposed DNNs...
Automatic speech recognition is one of the challenging area in the field of speech signal processing. Automatic speech recognition technology converts speech signal into text. This paper presents the implementation of isolated kannada word recognizer using Vector Quantization (VQ) and Fuzzy-C Means (FCM) techniques. The paper compares and contrasts the recognition accuracies of FCM and k-means techniques...
The spatial pyramid feature learning methods, such as Spatial Pyramid Matching (SPM) and Sparse Coding based Spatial Pyramid Matching (ScSPM), have achieved significant performance in image categorization. While most of these methods are still based on manual-design features, such as SIFT, HOG and LBP, which limits the representation of data. In this paper, we propose a novel Sparse Autoencoder based...
Classification of speech signal is one of the most vital problems in speech perception and spoken word recognition. Although, there have been many studies on the classification of speech signals but the results are still limited. In this paper, we propose an image based approach for speech signal classification based on the combination of Local Naïve Bayes Nearest Neighbor (LNBNN) and Scale-invariant...
The main task of computer-aided diagnosis (CADx) is to differentiate the pathological stages to which each detected colorectal lesion belongs, especially to differentiate hyperplastic polyps, which are non-neoplastic and seldom show malignant potential, from neoplastic lesions, which are malignant or at risk for malignant transformation. If we could extract useful pattern information from detected...
This paper presents a Tibetan component representation learning method for component-based online handwritten Tibetan character recognition. In conventional methods, we designed features manually for Tibetan components. The hand-crafted features are often incomplete and decrease the component recognition accuracy, which influences component-based character recognition performance. To overcome the...
Perceptual quality evaluation of the retargeting image plays an important role in benchmarking different retargeting methods, as well as guiding or optimizing the retargeting process. The distortions introduced during the retargeting process are mainly categorized into shape distortion and content information loss [1]. The shape distortion measurement is critical to the evaluation of retargeting image...
This paper proposes a novel approach based on scale invariant feature transform (SIFT) and kernel sparse representation for traffic sign recognition in complex traffic scenes. This module consists of several steps. In the first stage, SIFT is introduced for feature extraction from samples and test targets, respectively. The features are mapping to the kernel space. In the second stage, we construct...
This study proposes a system to automaticallyidentify multiple singers in a long audio stream that may havesinging voices overlapping in time. The system is of great helpin handling the rapid proliferation of music data. To achievethis, an audio stream is segmented into a sequence ofconsecutive, non-overlapping, fixed-length clips using asliding window, and then undergoes solo/duetrecognition, single...
One of important steps in hybrid statistical-structural recognition method for handwritten characters is to label primitives for classifier training and label structural position information for structural recognition. In this paper, we propose a semi-automatic component (primitive) annotation method for online handwritten Tibetan character database. All samples of each character class are over-segmented...
Accent is a special trait of human speech that can deliver some information about a speaker's background. At the same time it is one of the profound factors that affects the intelligibility and performance of speech recognition systems (ASRs) if not delicately handled. Normally accent recognizer in the preceding stage offers subsystem training or adaptation strategy to improve the ASRs. Formant analysis...
In this paper, we propose a method for 3D facial expression recognition. The algorithm is composed of three steps. The first step is to extract the region of interested 3D face, some data preprocessing works, including face location, point cloud rotation and uniform distribution of points cloud, have been done in this step. Otherwise, the second step is feature extraction, novel features are extracted...
In this paper, we present a novel video fingerprinting algorithm which leverages the concept of perceptual similarity between different video sequences. Inspired by the popular structural similarity (SSIM) index, we quantify the perceptual similarity between different video sequences by proposing a perceptual distance metric (PDM) which is utilized in the matching stage of our proposed video fingerprinting...
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