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Facial Expression Recognition (FER) is a challenging topic in the image classification field. Some of its applications, such as driver assistance systems, require real-time response or demand methods that can run on low-resources devices. FER can be classified into conventional methods and deep learning methods. Deep learning-based methods have attracted much attention in recent years because of their higher performance even under challenging scenarios. However, deep learning-based methods rely on models that demand high computational resources. At the same time, conventional methods depend on hand-crafted features that may not perform well in different scenarios. In this context, some studies are pursuing to reduce the computational complexity of deep learning models while achieving similar results to those more complex models. But even these models with reduced complexity can require a lot of computational resources.

To tackle this problem, we propose ExpressionHop. ExpressionHop is based on a Successive Subspace Learning classification technique called PixelHop[1], which allows us to automatically extract meaningful features without the need for higher computational demanding models. As shown in Figure1, we first extract facial landmark patches from face images, and then use Pixelhop to extract feature. Discriminant feature test is utilized for feature selection before doing classification using logistic regression. As shown in table1, our model achieved higher or similar results compared to traditional and deep learning methods for JAFFE, CK+, and KDEF datasets. At the same time, a comparison of the number of parameters of the models indicates that the proposed model demands fewer computational resources even when compared to newer deep learning methods that rely on reduced complexity models.

— By Chengwei Wei and Rafael Luiz Testa

A progressive edge-guided generative model, called ProGenHop, is presented in this work.

The majority of existing generative models utilize neural networks to represent the underlying data distribution. Alternatively, ProGenHop offers a novel method based on Successive Subspace Learning (SSL) for feature extraction and image generation. The benefits of ProGenHop are its interpretability and significantly lower computational complexity, as opposed to compulationally-complex, black-box neural networks. ProGenHop maintains generation quality with a small training size. Moreover, ProGenHop is easily extendable to further generative model applications, such as attribute-guided image generation, super resolution, and high-resolution image generation.

A generative model learns a distribution for the underlying dataset during the training phase. During the generation phase, samples can be drawn from the distribution as new data. Most of the prior work like the GAN-based, VAE-based, and diffusion-based generative models utilize neural networks to learn complex non-linear transformations. In our work, we utilize the SSL pipeline for feature extraction. An SSL pipeline consists of consecutive Saab transformations. In essence, the SSL pipeline receives an RGB image and converts it into a feature vector. Since Saab transformation is a variant of Principle Component Analysis (PCA), it inherits PCA properties; One of these nice properties is that the Saab transformation generates feature vectors with uncorrelated components. This property facilitates the utilization of Gaussian priors for generative model training.

ProGenHop is an unconditional generative model which has a progressive approach in generating images: it starts the unconditional generation in a low-resolution regime, then sequentially increases the resolution via a cascade of conditional generation modules. ProGenHop has three modules, namely, Generation Core, Resolution Enhancer, and Quality Booster. The first module learns the distribution of low-resolution images using a Gaussian mixture model and performs unconditional image [...]

With the pervasive of image steganography algorithms in social media, image steganalysis becomes inevitably important nowadays. One of the most secure steganographic scheme is called content-adaptive steganography. WOW, S-UNIWARD, HUGO and HILL are all successful steganography algorithms of this kind. Since content-adaptive steganography will calculate embedding cost after they evaluate the cover image, and will tend to put more embeddings in complex regions. It makes it harder for image steganalyzers to detect if the image has been embedded information or not.

Our goal is to provide a data-driven method, which does not apply hand-crafted high-pass filtering in preprocess step or any neural network based architectures. We have unsupervised feature extraction and machine learning-based classifier to fulfill the task. Specifically, we first split input image into 3×3 blocks, and partition blocks into several groups based their embedding cost. We use Saab transform to extract features on blocks and make decision. The difference of soft decision scores on cover image and stego image are efficient for us to do image-wise decision. In order to find the embed locations in unseen images, we train an embed location classifier from block soft decision scores, as shown in Fig. 1. Based on embed location probability score from each group, we train the final image-wise ensemble classifier and give us the image-level decision, as shown in Fig.2 .

Compared to CNN-based steganalysis models, our method does not use end-to-end training and backward propagation. Therefore, it is very light-weight in terms of model size and memory usage. In the meantime, our method can beat all traditional steganalysis method and some benchmarking CNN-based model.

— by Yao Zhu

Graph-based semi-supervised learning has shown prominent performance in node classification task by exploiting the underlying manifold structure of data. Recently, an enhancement on the classical label propagation (LP) named GraphHop is proposed, which has outperformed the existing graph convolutional networks (GCNs) on various networks. Although the superior performance in GraphHop model is explained in the view of smoothening both node attribute and label signals, its mechanisms are still not fundamentally clear.

In this work, we develop deeper insights into the the GraphHop model from the point of regularization framework. We show that GraphHop model can be cast into an iterative approximated optimization of a particular regularization function on graphs. Then, based on this variational interpretation, we propose two approaches to address the limits in the GraphHop model due to the approximated optimization process. In particular, these are 1) additional aggregations in optimizing the label embeddings; 2) adaptively selecting of the reliable unlabeled samples for the classifier training. Experiments show that equipped with these two improvements, our model called GraphHop++ is able to gain significantly better performance than the former GraphHop model, in addition to the state-of-the-art methods on various benchmark networks with limited label rates.

— By Tian Xie

Classification-oriented machine learning models have been well-studied in the past decades. The focus has shifted to deep learning (DL) in recent years. Feature learning and classification are handled jointly in DL models. Although the best performance of classification tasks is often achieved by DL through back propagation (BP), DL models suffer from lack of interpretability, high computational cost and high model complexity. Feature extraction and classification are treated as separate modules in classical machine learning. We focus on the classical learning paradigm and propose a new high-performance classifier with features as the input. Examples of classical classifiers include support vector machine (SVM), decision tree (DT) , multilayer perceptron(MLP) feedforward multilayer perceptron(FF-MLP) and extreme learning machine (ELM). SVM, DT and FF-MLP share one common idea, i.e., feature space partitioning. Inspired by the MLP, the DT and the ELM, a new classification model, called the subspace learning machine (SLM), is proposed aiming at general classification tasks.

The SLM attempts to efficiently partition the input feature space into multiple discriminant subspaces in a hierarchical manner and it works as follows: First, SLM identifies a discriminant subspace by examining the discriminant power of input features. Then, it applies random projections to input discriminant subspace features to yield p 1D subspaces and finds optimal partitions in each of them. This is equivalent to partitioning input space with p hyper-planes whose orientations and biases are determined by random projections and partitions, respectively.  Among p projections, we develop a criterion to choose the best q partitions that yield 2q partitioned subspaces. The subspace partitioning process is repeated at each child node.  When the samples are sufficiently pure at a child node, the partitioning process stops and SLM makes final predictions. SLM offers [...]