Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

Automated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems.     

Charting-based subspace learning for video-based human action classification

We use charting, a non-linear dimensionality reduction algorithm, for articulated human motion classification in multi-view sequences or 3D data. Charting estimates automatically the intrinsic dimensionality of the latent subspace and preserves local neighbourhood and global structure of high-dimensional data. We classify human actions sub-sequences of varying lengths of skeletal poses, adopting a multi-layered subspace classification scheme with layered pruning and search. The sub-sequences of varying lengths of skeletal poses can be extracted using either markerless articulated tracking algorithms or markerless motion capture systems. We present a qualitative and quantitative comparison of single-subspace and multiple-subspace classification algorithms. We also identify the minimum length of action skeletal poses, required for accurate classification, using competing classification systems as the baseline. We test our motion classification framework on HumanEva, CMU, HDM05 and ACCAD mocap datasets and achieve similar or better classification accuracy than various comparable systems.     

Unsupervised neural network learning procedures for feature extraction and classification

In this article, we review unsupervised neural network learning procedures which can be applied to the task of preprocessing raw data to extract useful features for subsequent classification. The learning algorithms reviewed here are grouped into three sections: information-preserving methods, density estimation methods, and feature extraction methods. Each of these major sections concludes with a discussion of successful applications of the methods to real-world problems.The first author is supported by research grants from the James S. McDonnell Foundation (grant #93–95) and the Natural Sciences and Engineering Research Council of Canada. For part of this work, the second author was supported by a Temporary Lectureship from the Academic Initiative of the University of London, and by a grant (GR/J38987) from the Science and Engineering Research Council (SERC) of the UK.     

Structured learning of local features for human action classification and localization

Human action recognition is a promising yet non-trivial computer vision field with many potential applications. Current advances in bag-of-feature approaches have brought significant insights into recognizing human actions within complex context. It is, however, a common practice in literature to consider action as merely an orderless set of local salient features. This representation has been shown to be oversimplified, which inherently limits traditional approaches from robust deployment in real-life scenarios. In this work, we propose and show that, by taking into account global configuration of local features, we can greatly improve recognition performance. We first introduce a novel feature selection process called Sparse Hierarchical Bayes Filter to select only the most contributive features of each action type based on neighboring structure constraints. We then present the application of structured learning in human action analysis. That is, by representing human action as a complex set of local features, we can incorporate different spatial and temporal feature constraints into the learning tasks of human action classification and localization. In particular, we tackle the problem of action localization in video using structured learning with two alternatives: one is Dynamic Conditional Random Field from probabilistic perspective; the other is Structural Support Vector Machine from max-margin point of view. We evaluate our modular classification-localization framework on various testbeds, in which our proposed framework is proven to be highly effective and robust compared against bag-of-feature methods.     

Adaptive learning codebook for action recognition

Learning a compact and yet discriminative codebook is an important procedure for local feature-based action recognition. A common procedure involves two independent phases: reducing the dimensionality of local features and then performing clustering. Since the two phases are disconnected, dimensionality reduction does not necessarily capture the dimensions that are greatly helpful for codebook creation. What’s more, some dimensionality reduction techniques such as the principal component analysis do not take class separability into account and thus may not help build an effective codebook. In this paper, we propose the weighted adaptive metric learning (WAML) which integrates the two independent phases into a unified optimization framework. This framework enables to select indispensable and crucial dimensions for building a discriminative codebook. The dimensionality reduction phase in the WAML is optimized for class separability and adaptively adjusts the distance metric to improve the separability of data. In addition, the video word weighting is smoothly incorporated into the WAML to accurately generate video words. Experimental results demonstrate that our approach builds a highly discriminative codebook and achieves comparable results to other state-of-the-art approaches.     

Unsupervised learning for human activity recognition using smartphone sensors

To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Caliński–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand.     

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Since learning English is very popular in non-English speaking countries, developing modern assisted-learning tools that support effective English learning is a critical issue in the English-language education field. Learning English involves memorization and practice of a large number of vocabulary words and numerous grammatical structures. Vocabulary learning is a principal issue for English learning because vocabulary comprises the basic building blocks of English sentences. Therefore, many studies have attempted to improve the efficiency and performance when learning English vocabulary. With the accelerated growth in wireless and mobile technologies, mobile learning using mobile devices such as PDAs, tablet PCs, and cell phones has gradually become considered effective because it inherits all the advantages of e-learning and overcomes limitations of learning time and space that limit web-based learning systems. Therefore, this study presents a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycle, which recommends appropriate English vocabulary for learning according to individual learner vocabulary ability and memory cycle. The proposed system has been successfully implemented on personal digital assistant (PDA) for personalized English vocabulary learning. The experimental results indicated that the proposed system could obviously promote the learning performances and interests of learners due to effective and flexible learning mode for English vocabulary learning.     

Contextually relevant pedagogical agents: Visual appearance,stereotypes, and first impressions and their impact on learning

Humans draw on their stereotypic beliefs to make assumptions about others. Even though prior research has shown that individuals respond socially to media, there is little evidence with regards to learners stereotyping and categorizing pedagogical agents. This study investigated whether learners stereotype a pedagogical agent as being knowledgeable or not knowledgeable and how this acuity influenced learning. Participants were assigned to four experimental conditions differing by agent (scientist or artist) and tutorial type (nanotechnology or punk rock). Quantitative analyses indicated that agents were stereotyped depending on their image and the academic domain under which they functioned. Regardless of tutorial, participants assigned to the artist agent recalled more information than participants assigned to the scientist agent. Learning differences between the groups varied according to whether agent appearance fit the content area under investigation. Qualitative results indicated learner's stereotypic expectations as well as their unwillingness to draw conclusions based on visual appearance.     

An investigation of temporal variability of the total column ozone over tropical urban,high altitude,and coastal stations in Western Maharashtra,India

Distribution and variability of ozone are vital to the atmospheric thermal structure as it can exert great influence on climate. In this study, the Microtops II Ozonometer (Microtops)-measured total column ozone (TCO) data archived at the tropical urban, high altitude, and coastal observing sites during 2012–2015 are analysed to investigate the temporal structure of ozone. Results reveal that the TCO exhibits a non-negligible diurnal variability depicting distinct seasonal behaviour, which corroborates well with the Indian as well as the worldwide measurements of TCO. The mean rate of ozone diurnal change (V_s) in winter is found to be maximum (approximately 2.1 DU h^–1) while it is minimum (about 0.53 DU h^–1) in pre-monsoon. In spite of the prevalent variability of the order of about 2–9 DU amongst Microtops channels and Ozone Monitoring Instrument on board the NASA EOS/AURA spacecraft (OMI-AURA) measurements, there exists a strong monthly/seasonal variation in both the ground- and satellite-based TCO measurements. Monthly mean OMI-AURA TCO variation presents a nearly perfect sinusoidal wave with a coefficient of determination (R²) equal to 0.76. Monthly TCO is maximum in May/June and minimum in December/January. The noticeable diurnal and monthly TCO variability could be due to a complex combination of photochemical processes in the lower troposphere and the transport in the middle and upper troposphere. Linear regression technique applied to the Microtops and OMI-AURA data sets show that the two data sets are better correlated with a correlation coefficient (r) taking values 0.71, 0.77, and 0.61 for channels I, II, and III, respectively. The three Microtops channels show the dispersion of about 8–11 DU around 1:1 regression line which is of the order of one standard deviation of the daily mean data set. The TCO data at all Microtops channels either underestimate or overestimate with respect to the OMI-AURA measurements since the values for slopes of the linear regression line for all the three channels are ≤1. Pearson’s product moment correlation analysis indicates that the TCO anti-correlates with ultraviolet-B (UV-B) irradiance (vis-à-vis through UV index) as the Pearson’s product moment correlation coefficients are found to be in the range –0.52 to –0.97.     

基于软迁移和渐进式学习的人体行为识别方法

基于"学习者-监督者"的间接学习机制,提出多阶段监督的软迁移学习方法来实现跨网络结构学习,使神经网络对人体行为的建模能力能在不同结构的网络中传递和重用.根据数据特征在不同网络层级上的不同特性,引入两种有效的特征差异度量函数,降低不同网络结构提取的特征之间的差异.在UCF101和HMDB51数据集上进行实验,其结果表明,...     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。     

Joint prototype and metric learning for image set classification: Application to video face identification

In this paper, we address the problem of image set classification, where each set contains a different number of images acquired from the same subject. In most of the existing literature, each image set is modeled using all its available samples. As a result, the corresponding time and storage costs are high. To address this problem, we propose a joint prototype and metric learning approach. The prototypes are learned to represent each gallery image set using fewer samples without affecting the recognition performance. A Mahalanobis metric is learned simultaneously to measure the similarity between sets more accurately. In particular, each gallery set is represented as a regularized affine hull spanned by the learned prototypes. The set-to-set distance is optimized via updating the prototypes and the Mahalanobis metric in an alternating manner. To highlight the importance of representing image sets using fewer samples, we analyzed the corresponding test time complexity with respect to the number of images used per set. Experimental results using YouTube Celebrity, YouTube Faces, and ETH-80 datasets illustrate the efficiency on the task of video face recognition, and object categorization.     

Building Visual Vocabulary for Image Indexation and Query Formulation

With advances in digital imaging, the amount of digital images will increase tremendously. To locate relevant images in a large collection of images presents a challenging and genuine problem for content-based retrieval research. This paper presents a novel framework called visual keywords for image indexation and query formulation. Visual keywords are flexible and intuitive visual prototypes specified perceptually from sample domain images. A visual content is described and indexed by flexible spatial aggregation of the soft presence of visual keywords. A new query method based on visual constraints is also proposed to allow direct and explicit content specification. Last but not least, we have developed a digital album prototype to demonstrate query and retrieval on both home photos and stock photos based on visual keywords.     

在线学习视频索引与检索方法

针对交互式的多媒体学习系统的特点,提出了一种基于自然语言的方法来实现基于内容的视频检索,用户可以用自然语言和系统进行交互,从而方便快捷地找到自己想要的视频片段.该方法集成了自然语言处理、实体名提取,基于帧的索引以及信息检索等技术,从而使系统能够处理用户提出的自然语言问题,根据问题构建简洁明了的问题模板,用问题模板与系统中已建的描述视频的模板进行匹配,从而降低了视频检索问题的复杂度,提高了系统的易用性.     

Effects of learning for linear and differential video magnifiers

This study sought to determine the learning effects of repeated practice with the traditional linear and the novel differential linear and differential non-linear magnification methods on visual inspection performance. Performance feedback of speed and accuracy and process feedback of scan paths and coverage of search area were given to subjects in order to facilitate the learning process. Objective performance in terms of speed and accuracy and subjective evaluation using the NASA Task Load Index paradigm were captured and analysed. The results showed that there were positive learning effects for the three magnification methods and the learning effects for the two differential magnification methods were greater than that for the traditional linear method. Three exponential learning curves were established for the three search tasks, which showed that search performance with the differential linear and differential non-linear magnifications would surpass the traditional linear method after four and 10 sessions of repeated practice, respectively.     

Multilevel design of experiments for statistical moment and probability calculation

A procedure for statistical moment estimation and reliability analysis using design of experiment (DOE) is proposed. A numerical method of finding the optimal levels and weights of DOE for statistical moment estimation is established and applied to three- and five-level cases. The four statistical moments of the system response function are then calculated from the full-factorial DOE, and the probability distribution of the system response function is obtained using the empirical distribution systems such as the Pearson system. The proposed method is tested through several examples and compared with other analysis methods, including the previous developments of a three-level full-factorial design. The results show that it relieves much of the difficulties met in the previous method and provides good accuracy compared to other methods for various input distributions.     

A simultaneous learning framework for clustering and classification

Traditional pattern recognition generally involves two tasks: unsupervised clustering and supervised classification. When class information is available, fusing the advantages of both clustering learning and classification learning into a single framework is an important problem worthy of study. To date, most algorithms generally treat clustering learning and classification learning in a sequential or two-step manner, i.e., first execute clustering learning to explore structures in data, and then perform classification learning on top of the obtained structural information. However, such sequential algorithms cannot always guarantee the simultaneous optimality for both clustering and classification learning. In fact, the clustering learning in these algorithms just aids the subsequent classification learning and does not benefit from the latter. To overcome this problem, a simultaneous learning framework for clustering and classification (SCC) is presented in this paper. SCC aims to achieve three goals: (1) acquiring the robust classification and clustering simultaneously; (2) designing an effective and transparent classification mechanism; (3) revealing the underlying relationship between clusters and classes. To this end, with the Bayesian theory and the cluster posterior probabilities of classes, we define a single objective function to which the clustering process is directly embedded. By optimizing this objective function, the effective and robust clustering and classification results are achieved simultaneously. Experimental results on both synthetic and real-life datasets show that SCC achieves promising classification and clustering results at one time.