结合组稀疏效应和多核学习的图像标注

图像中存在的纹理、颜色和形状等异构视觉特征,在表示特定高层语义时所起作用的重要程度不同,为了在图像标注过程中更加有效地利用这些异构特征,提出了一种基于组稀疏(group sparsity)的多核学习方法(multiple kernel learning with group sparsity,简称MKLGS),为不同图像语义选择不同的组群特征.MKLGS先将包含多种异构特征的非线性图像数据映射到一个希尔伯特空间,然后利用希尔伯特空间中的核函数以及组LASSO(groupLASSO)对每个图像类别选择最具区别性特征的集合,最终训练得到分类模型对图像进行标注.通过与目前其他图像标注算法进行对比,实验结果表明,基于组稀疏的多核学习方法在图像标注中能取得很好的效果.     

Computer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps

In this paper, we show that zoom-endoscopy images can be well classified according to the pit-pattern classification scheme by using texture-analysis methods in different wavelet domains. We base our approach on three different variants of the wavelet transform and propose that the color channels of the RGB and LAB color model are an important source for computing image features with high discriminative power. Color-channel information is incorporated by either using simple feature vector concatenation and cross-cooccurrence matrices in the wavelet domain. Our experimental results based on k-nearest neighbor classification and forward feature selection exemplify the advantages of the different wavelet transforms and show that color-image analysis is superior to grayscale-image analysis regarding our medical image classification problem.     

An improved global feature selection scheme for text classification

Feature selection is known as a good solution to the high dimensionality of the feature space and mostly preferred feature selection methods for text classification are filter-based ones. In a common filter-based feature selection scheme, unique scores are assigned to features depending on their discriminative power and these features are sorted in descending order according to the scores. Then, the last step is to add top-N features to the feature set where N is generally an empirically determined number. In this paper, an improved global feature selection scheme (IGFSS) where the last step in a common feature selection scheme is modified in order to obtain a more representative feature set is proposed. Although feature set constructed by a common feature selection scheme successfully represents some of the classes, a number of classes may not be even represented. Consequently, IGFSS aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in IGFSS to label features according to their discriminative power on classes and these labels are used while producing the feature sets. Experimental results on well-known benchmark datasets with various classifiers indicate that IGFSS improves the performance of classification in terms of two widely-known metrics namely Micro-F1 and Macro-F1.     

Smile detection in the wild with deep convolutional neural networks

Smile or happiness is one of the most universal facial expressions in our daily life. Smile detection in the wild is an important and challenging problem, which has attracted a growing attention from affective computing community. In this paper, we present an efficient approach for smile detection in the wild with deep learning. Different from some previous work which extracted hand-crafted features from face images and trained a classifier to perform smile recognition in a two-step approach, deep learning can effectively combine feature learning and classification into a single model. In this study, we apply the deep convolutional network, a popular deep learning model, to handle this problem. We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously. Experimental results demonstrate that although a deep learning model is generally developed for tackling “big data,” the model can also effectively deal with “small data.” We further investigate into the discriminative power of the learned features, which are taken from the neuron activations of the last hidden layer of our Smile-CNN. By using the learned features to train an SVM or AdaBoost classifier, we show that the learned features have impressive discriminative ability. Experiments conducted on the GENKI4K database demonstrate that our approach can achieve a promising performance in smile detection.     

Selection and fusion of color models for image feature detection

The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model induces the equivalence classes to the actual algorithms. As there are many color models available, the inherent difficulty is how to automatically select a single color model or, alternatively, a weighted subset of color models producing the best result for a particular task. The subsequent hurdle is how to obtain a proper fusion scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color model selection and fusion of feature detection algorithms, in this paper, we propose a method that exploits nonperfect correlation between color models or feature detection algorithms derived from the principles of diversification. As a consequence, a proper balance is obtained between repeatability and distinctiveness. The result is a weighting scheme which yields maximal feature discrimination. The method is verified experimentally for three different image feature detectors. The experimental results show that the fusion method provides feature detection results having a higher discriminative power than the standard weighting scheme. Further, it is experimentally shown that the color model selection scheme provides a proper balance between color invariance (repeatability) and discriminative power (distinctiveness)     

Discriminative and deterministic approaches towards entity resolution

To address the entity resolution problem, existing studies usually consist of two-steps. Given two lists of records, in the first step a small set of duplicate records (a candidate set) are selected based on index structures and algorithms for efficient entity resolution. Then, a given similarity function is applied to quantify the similarity of records in the candidate set. However, for real applications, it is a non-trivial task to select appropriate indexing techniques and similarity functions. In this paper, we tackle the problem of indexing and similarity function identification using both discriminative and deterministic approaches that select the best of indexing and similarity measures. According to our experimental results, our proposed solution considering both discriminative and deterministic approaches shows more than a 90 % average accuracy within hundreds of seconds.     

一种新颖的对比子图索引算法

针对当前图索引算法存在的问题,提出一种基于对比子图索引框架,开发冗余感知机制,选择一个小型的具有明显区分力的索引特征集,改善索引性能。实验结果表明,该算法对不同的包容搜索载荷能达到近优化的修剪力,与传统图搜索方法相比,具有明显的索引性能优势。     

A hybrid probabilistic framework for content-based image retrieval with feature weighting

In this paper, we propose a probabilistic framework for efficient retrieval and indexing of image collections. This framework uncovers the hierarchical structure underlying the collection from image features based on a hybrid model that combines both generative and discriminative learning. We adopt the generalized Dirichlet mixture and maximum likelihood for the generative learning in order to estimate accurately the statistical model of the data. Then, the resulting model is refined by a new discriminative likelihood that enhances the power of relevant features. Consequently, this new model is suitable for modeling high-dimensional data described by both semantic and low-level (visual) features. The semantic features are defined according to a known ontology while visual features represent the visual appearance such as color, shape, and texture. For validation purposes, we propose a new visual feature which has nice invariance properties to image transformations. Experiments on the Microsoft's collection (MSRCID) show clearly the merits of our approach in both retrieval and indexing.     

基于多尺度LBP金字塔特征的分类算法

为有效解决旋转变化、光照变化和尺度变化等图像的分类问题,提出一种基于多尺度局部二元模式(LBP)金字塔特征的图像分类算法。通过多尺度LBP金字塔提取各尺度的图像纹理特征,建立图像的多尺度LBP金字塔直方图,并将其作为图像特征向量,采用K-means方法对该特征向量进行降维,以用于图像分类。同时,针对传统二进制权值分布方法对噪声敏感的缺点,提出一种多端权值分布方法。实验结果表明,多尺度LBP金字塔方法具有较好的可鉴别性及图像描述能力,而多端权值分布法也能提高图像的分类精度。     

Visual tracking based on Distribution Fields and online weighted multiple instance learning

This paper presents an improved multiple instance learning (MIL) tracker representing target with Distribution Fields (DFs) and building a weighted-geometric-mean MIL classifier. Firstly, we adopt DF layer as feature instead of traditional Haar-like one to model the target thanks to the DF specificity and the landscape smoothness. Secondly, we integrate sample importance into the weighted-geometric-mean MIL model and derive an online approach to maximize the bag likelihood by AnyBoost gradient framework to select the most discriminative layers. Due to the target model consisting of selected discriminative layers, our tracker is more robust while needing fewer features than the traditional Haar-like one and the original DFs one. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.     

Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors

This paper addresses human detection and pose estimation from monocular images by formulating it as a classification problem. Our main contribution is a multi-class pose detector that uses the best components of state-of-the-art classifiers including hierarchical trees, cascades of rejectors as well as randomized forests. Given a database of images with corresponding human poses, we define a set of classes by discretizing camera viewpoint and pose space. A bottom-up approach is first followed to build a hierarchical tree by recursively clustering and merging the classes at each level. For each branch of this decision tree, we take advantage of the alignment of training images to build a list of potentially discriminative HOG (Histograms of Orientated Gradients) features. We then select the HOG blocks that show the best rejection performances. We finally grow an ensemble of cascades by randomly sampling one of these HOG-based rejectors at each branch of the tree. The resulting multi-class classifier is then used to scan images in a sliding window scheme. One of the properties of our algorithm is that the randomization can be applied on-line at no extra-cost, therefore classifying each window with a different ensemble of randomized cascades. Our approach, when compared to other pose classifiers, gives fast and efficient detection performances with both fixed and moving cameras. We present results using different publicly available training and testing data sets.     

Legendre polynomials based feature extraction for online signature verification. Consistency analysis of feature combinations

In this paper, feature combinations associated with the most commonly used time functions related to the signing process are analyzed, in order to provide some insight on their actual discriminative power for online signature verification. A consistency factor is defined to quantify the discriminative power of these different feature combinations. A fixed-length representation of the time functions associated with the signatures, based on Legendre polynomials series expansions, is proposed. The expansion coefficients in these series are used as features to model the signatures. Two different signature styles, namely, Western and Chinese, from a publicly available Signature Database are considered to evaluate the performance of the verification system. Two state-of-the-art classifiers, namely, Support Vector Machines and Random Forests are used in the verification experiments. Error rates comparable to the ones reported over the same signature datasets in a recent Signature Verification Competition, show the potential of the proposed approach. The experimental results, also show that there is a good correlation between the consistency factor and the verification errors, suggesting that consistency values could be used to select the optimal feature combination.     

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body. Most of this work was done while the first author was with Boston University.     

Efficient logo recognition by local feature groups

This paper presents a method for efficient and scalable logo recognition. Using generalized Hough transform to identify local features that are invariant across images, we can efficiently add spatial information into groups of local features and enhance the discriminative power of local feature. Our method is more flexible and efficient compared with state-of-the-art methods that merge features into groups. To fully exploit the information that different logo images provide, we employ a reference-based image representation scheme to represent training and testing images. Experiments on challenging datasets show that our method is efficient and scalable and achieves state-of-the-art performance.     

Non-negative locality-constrained vocabulary tree for finger vein image retrieval

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency.However, there is a large accumulative quantization error in the vocabulary tree (VT)model thatmay degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performance and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performance than other state-of-theart methods, while maintaining low time complexity.     

基于核稀疏表示的特征选择算法*

为了解决高维数据在分类时导致的维数灾难,降维是数据预处理阶段的主要步骤。基于稀疏学习进行特征选择是目前的研究热点。针对现实中大量非线性可分问题,借助核技巧,将非线性可分的数据样本映射到核空间,以解决特征的非线性相似问题。进一步对核空间的数据样本进行稀疏重构,得到原数据在核空间的一种简洁的稀疏表达方式,然后构建相应的评分机制选择最优子集。受益于稀疏学习的自然判别能力,该算法能够选择出保持原始数据结构特性的"好"特征,从而降低学习模型的计算复杂度并提升分类精度。在标准UCI数据集上的实验结果表明,其性能上与同类算法相比平均可提高约5%。     

Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning

In this paper, we propose a new feature selection method called kernel fisher discriminant analysis and regression learning based algorithm for unsupervised feature selection. The existing feature selection methods are based on either manifold learning or discriminative techniques, each of which has some shortcomings. Although some studies show the advantages of two-steps method benefiting from both manifold learning and discriminative techniques, a joint formulation has been shown to be more efficient. To do so, we construct a global discriminant objective term of a clustering framework based on the kernel method. We add another term of regression learning into the objective function, which can impose the optimization to select a low-dimensional representation of the original dataset. We use L_2,1-norm of the features to impose a sparse structure upon features, which can result in more discriminative features. We propose an algorithm to solve the optimization problem introduced in this paper. We further discuss convergence, parameter sensitivity, computational complexity, as well as the clustering and classification accuracy of the proposed algorithm. In order to demonstrate the effectiveness of the proposed algorithm, we perform a set of experiments with different available datasets. The results obtained by the proposed algorithm are compared against the state-of-the-art algorithms. These results show that our method outperforms the existing state-of-the-art methods in many cases on different datasets, but the improved performance comes with the cost of increased time complexity.

     

Learning flexible features for conditional random fields

Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features. These features can be functions of arbitrary combinations of observations, labels and auxiliary hidden variables. We also present a simple induction scheme to learn these features, which can automatically determine the complexity needed for a given data set. We apply the model to two real-world tasks, information extraction and image labeling, and compare our results to several other methods for discriminative labeling.     

A Study on Automatic Classification of Users’ Desktop Interactions

Knowledge workers frequently change activities, either by choice or through interruptions. With an increasing number of activities and activity switches, it is becoming more and more difficult for knowledge workers to keep track of their desktop activities. This article presents our efforts to achieve activity awareness through automatic classification of user's everyday desktop activities. For getting a deeper understanding, we investigate performance of various classifiers with respect to discriminative power of time-, interaction-, and content-based feature sets for different work scenarios and users. Specifically, by viewing an activity as a sequence of desktop interactions we present (1) a methodology for translating a user's desktop interactions into activities, (2) evaluation of the discriminative power of different activity features and feature types, and (3) analysis of supervised classification models for classifying desktop activity under two different scenarios, i.e., an activity-centric scenario and a user-centric scenario. The experiments are carried out on a real-world dataset, and the results show satisfactory accuracy using relatively few and simple types of features.