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Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt...
Many noninvasive continuous blood pressure measurements using photoplethysmography (PPG) are still inadequate in terms of accuracy and stability, which hinders the practical application of this method. This paper proposes a model based on ensemble method for BP estimation using PPG. A number of blood pressure calculation base-models is built on the same training data. These base-models are used to...
We present a method that improves the objective quality estimation of a speech utterance. We show that including raw features that are presumably redundant reduces the effect of input noise and improves the performance of linear regressors. To exploit this effect we propose the novel idea to augment the feature set with redundant features. The proposed augmented feature set and the neural network...
BGM (background music) of a video plays an important role for making a video impressive. Although a large number of royalty-free music clips are available on the web, it is still difficult for amateur video creators to select appropriate music clips for their videos. In this paper, we propose a computational method for estimating the impression of a video from auditory and visual features of a video...
The building, being one of the largest energy consumers, accounts for over 40% of the global energy consumption. The traditional Heating, Ventilation and Air Conditioning (HVAC) and lighting systems cause considerable energy wastes in the building since they cannot adapt to the time-varying occupancy levels, which is expensive to measure with dedicated sensing systems. We propose an indirect method...
Classification between foggy and non-foggy images is a primitive step for automation in traffic activity and industries. The existing techniques provide low accuracy and needs validation over both synthetic and natural database. Foggy images are identified and classified based on their optical characteristics for vision enhancement and to make them more efficient for further processing. In proposed...
Social networking sites such as Twitter provide more opportunities to express what people think or intend in short text. In short text, abbreviations such as "ASAP" or "joinus" and emoticons are often used. Because these expressions are not registered into the existing dictionaries, these are analyzed as unknown expressions. That can be a bottleneck for improving accuracy of reputation...
Feature selection and learning through selected features are the two steps that are generally taken in classification applications. Commonly, each of these tasks are dealt with separately. In this paper, we introduce a method that optimally combines feature selection and learning through feature-based models. Our proposed method implicitly removes redundant and irrelevant features as it searches through...
Supervised classification algorithms such as Boosting and SVM have achieved significant success in the field of computer vision for classification and object recognition. However, the performance of the classifier decreases rapidly if there are insufficient labeled training samples. In this paper, a semi-supervised boosting algorithm is proposed to overcome this limitation. First, a few labeled instances...
Pose estimation is the most important step of nature interactive between human and machine, and body part recognition is the core of pose estimation. This paper describes an improved random forests method to recognize each part of the human body. What is different from the traditional random forest structure is that the algorithm proposed in this paper provides a feature Pre - selection for examples...
This paper presents a text detection method that combines with an image rectification. Since texts in natural scenes are not always observed in frontal view, image rectification is needed to robustly recognize them in OCR. A reference pixel that is part of the desirable text area is given by user since this is the easiest way to give the priors. In text detection, first, foreground pixels are extracted...
We present a general method for tackling the related problems of pose estimation of known object instances and object categories. By representing the training images as a probability distribution over the joint appearance/pose space, the method is naturally suitable for modeling the appearance of a single instance of an object, or of diverse instances of the same category. The training data is weighted...
This paper addresses the problem of full pose estimation of objects in 2D images, using registered 2D examples as training data. We present a general formulation of the problem, which departs from traditional approaches by not focusing on one specific type of image features. The proposed algorithm avoids relying on specific model-to-scene correspondences, allowing using similar-looking and generally...
This paper proposes contour-based features for articulated pose estimation. Most of recent methods are designed using tree-structured models with appearance evaluation only within the region of each part. While these models allow us to speed up global optimization in localizing the whole parts, useful appearance cues between neighboring parts are missing. Our work focuses on how to evaluate parts...
We apply and compare a random Bayes forest classifier and three traditional classification methods to a dataset of complex benthic macroinvertebrate images of known taxonomical identity. Since in biomonitoring changes in benthic macroinvertebrate taxa proportions correspond to changes in water quality, their correct estimation is pivotal. As classification errors are passed on to the allocated proportions,...
Domain adaptation is an important problem in learning to rank due to the lack of training data in a new search task. Recently, an approach based on instance weighting and pairwise ranking algorithms has been proposed to address the problem by learning a ranking model for a target domain only using training data from a source domain. In this paper, we propose a novel framework which extends the previous...
Automatic age estimation from facial images has aroused research interests in recent years due to its promising potential for some computer vision applications. Among the methods proposed to date, personalized age estimation methods generally outperform global age estimation methods by learning a separate age estimator for each person in the training data set. However, since typical age databases...
As more and more multimedia data become available on the Web, mining on those data is playing an increasingly important role in Web applications. In this paper, we investigate the interplay between multimedia data mining and text data mining. Specifically, in an approach we called text-aided image classification (TAIC), we address the problem of image classification with very limited amount of labeled...
An important property of brain signals is their nonstationarity. How to adapt a Brain-Computer Interface (BCI) to the changing brain states is one of the challenges faced by BCI researchers, especially in a real application scenario where the subject's real intent is unknown to the system. In this paper, an unsupervised approach based on Fuzzy C-Means (FCM) algorithm is proposed for the online adaptation...
A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. Specifically, we train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features...
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