Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.
. A number of specialized algorithms have been proposed to address the LDL task, and have shown their effectiveness in many computer vision applications, such as facial age estimation @cite_10 @cite_22 @cite_14 , expression recognition @cite_29 and hand orientation estimation @cite_18 .
@cite_18 @cite_14 @cite_22 @cite_29 @cite_10
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We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98 on the training set and over 97 on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques. By observing that the faces at close ages are similar, some Label Distribution Learning (LDL) methods have been proposed to solve age estimation tasks that they treat age distributions as the training targets. However, these existent LDL methods are limited because they can hardly extract enough useful information from complex image features. In this paper, Sparsity Conditional Energy Label Distribution Learning (SCE-LDL) is proposed to solve this problem. In the proposed SCE-LDL, age distributions are used as the training targets and energy function is utilized to define the age distribution. By assigning a suitable energy function, SCE-LDL can learn distributed representations, which provides the model with strong expressiveness for capturing enough of the complexity of interest from image features. The sparsity constraints are also incorporated to ameliorate the model. Experiment results in two age datasets show remarkable advantages of the proposed SCE-LDL model over the previous proposed age estimation methods. We present a multi-purpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single deep convolutional neural network (CNN). The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks. Extensive experiments show that the network has a better understanding of face and achieves state-of-the-art result for most of these tasks. A novel discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition. The face image is first partitioned into several non-overlapping regions. In each region, multi-scale local binary uniform pattern histograms1 are extracted and concatenated into a regional feature. The features are then projected on the LDA space to be used as a discriminative facial descriptor. The method is implemented and tested in face identification on the standard Feret database and in face verification on the XM2VTS database with very promising results.