Miracle-dz/UniFAR-FTUs huggingface dataset:
General-to-Specialized Analysis Based on Deep Belief Network

Recently, the deep neural network has achieved great performance in many areas. During analysis, all learned features are used at once, some of which could bring negative affect to specific classes. Recently, cognitive studies show that a human visual cognition process is hierarchical and dynamic, i.e., when meeting different targets, human brain intends to pay attention to different parts. Therefore, in this paper, we introduce this kind of mechanism into deep belief network (DBN) and propose a new general-to-specialized algorithm. Firstly, hierarchical knowledge networks are constructed based on the original learned DBN through pruning and retraining. Because these networks are learned for different discriminate ability, we call them as the general network and the specialized network separately. Secondly, a general-to-specialized analysis method is proposed which is proved theoretically. When predicting the class of an input sample, we select the corresponding specialized networks according to the preliminary analysis result and then make in-depth analysis. Experiments on four benchmark datasets are performed to test the proposed algorithm. The results show that our algorithm is feasible, valid and robust.
Applying BinaryWeights in Neural Networks for Sequential Text Recognition

With the development of deep learning, researchers have achieved lots of breakthroughs in many classical problems. Unfortunately, these progresses are demanding on the hardwares, especially GPU, causing a huge energy consumption. Therefore, how to implement these neural networks with lower requirements of hardwares is holding more and more attention. In this paper, two aspects of the work were done. Firstly, we build a deep learning framework that supports training and prediction with binarized neural networks. This framework include binarized layers, e.g. Convolution (Conv) and LSTM layers. It is based on our analysis of how to implement a binarized layer and train BianryWeights with it. Secondly, we construct a network with binarized layers which are implemented in our framework to achieve good performance on sequential text recognition. We also modify the network architecture in order to obtain the experimental results with little loss on accuracy.
An Enhanced Density Peak Based Clustering Algorithm

The density peak (DP) based clustering algorithm is a potential clustering approach proposed recently. The DP algorithm relies on local density and a carefully designed distance measure to identify cluster centers and members. Local density estimation is a key step in the DP algorithm as it influences both distance calculation and cluster center identification. In this paper we firstly study the cutoff and Gaussian density kernels and present a new density kernel based on the average distance to neighboring data. More importantly, we present a new criterion to identify cluster centers by making use of the density relationship among neighboring data. Experiments on various datasets validate the effectiveness of our approach.
Depth Estimation for Hazy Images Using Deep Learning

3D scene understanding is important for many applications in the computer vision field. However, the majority of existing solutions commonly assume the images to be captured in clear media. In real world situations, we may encounter less than ideal conditions, for example haze or fog. In these cases, the captured images will contain scattering and veiling effects that obscure the features of the scene. Many studies approach these images by first removing the scattering effects to obtain an approximate clear image. However, by studying the physical model of light propagation in scattering media, we have observed a relation between the captured image intensity and the distance from the camera. Therefore, as a contrast, we attempt to exploit these scattering effects to obtain 3D depth cues. In order to learn the relation between the scattering effects and the depth, we utilize deep networks to help extract and build high-level features. In this paper, we propose a novel classification approach for depth map estimation of hazy images using deep learning.
Fully Convolutional DenseNet for Saliency-Map Prediction

In this paper, we propose a fully convolutional DenseNet model for saliency-map prediction (DenseSal). While the most state-of-the-art models for predicting saliency maps use shallow networks such as VGG-16, our model uses densely connected convolutional networks (DenseNet) with over 150 layers. Since DenseNet has shown the excellent results on image classification tasks, the coarse feature maps from the fully convolutional neural networks based on DenseNets were concatenated to predict saliency maps through a readout network. It is shown that the DenseNet is useful for the saliency-map prediction and achieved the state-of-the-art accuracy on the major fixation datasets.
Compressing YOLO Network by Compressive Sensing

Object detection is one of the fundamental challenges in pattern recognition community. Recently, convolutional neural networks (CNN) are increasingly exploited in object detection, showing their promising potentials of generatively discovering patterns from quantity of labeled images. Among CNN-based systems, we focus on one state-of-the-art architecture designed for fast object detection, named as YOLO. However, YOLO, as well as CNN-based systems are hard to deploy on embedded systems due to their computationally and storage intensive. In this paper, we propose to compress YOLO network by compressive sensing, which exploits in-herent redundancy property of parameters in layers of CNN architecture, leading to decrease the computation and storage cost. We firstly convert parameter matrix to frequency domain through discrete cosine transform (DCT). Due to the smooth property of parameters when processing images, the resulting frequency matrix are dominated by low-frequency components. Next, we prune high-frequency part to make the frequency matrix sparse. After pruning, we sample the frequency matrix with distributed random Gaussian matrix. Finally, we retrain the network to finetune the remaining parameters. We evaluate the proposed compress method on VOC 2012 dataset and show it outperforms one latest compression approach.
PM2.5 Concentration Estimation Based on Image Quality Assessment

PM2.5 is an important indicator of air pollution. Fine-grained PM2.5 particulate data can prevent people from long time exposure. In this paper, by considering the influence of relative humidity we propose a new model to estimate PM2.5 concentration based on the quality of images taken by smartphones. First, the air quality related image feature, namely the transmission map, was generated according to dark channel prior. Then the transmission was corrected by relative humidity. Finally, a robust linear regression was used to construct the PM2.5 prediction model. The experiments with several datasets over one month demonstrate that the proposed method can achieve higher prediction accuracy of PM2.5 concentration than state-ofthe- art methods, which is meaningful for air quality monitoring in daily life.
Target Code Guided Binary Hashing Representations with Deep Neural Network

Most existing hashing approaches usually impose some artificial constraints (e.g., uncorrelated and balanced) on hash functions to learn high-quality binary codes, and utilize an optimization strategy which is typically compatible with these hash functions. However, these tight constraints potentially restrict the flexibility of hash functions to fit training data, and result in complicated optimization problem. In this paper, we propose a learning-based hashing method called 'deep supervised hashing with target code'(DSHT) to distill the desirable property in the target coding into hash functions to generate high-quality binary codes. Meanwhile, we incorporate the disparity learning of the intra-class into our proposed method for its generalization. Benefiting from recent advances in deep learning, our framework constructs hash functions as a latent hashing layer in a deep neural network in which binary hashing representations are learned with the guide of target code and semantic information. Experiments on two two large-scale image dataset (MNIST, CIFAR-10) demonstrate that the proposed framework is available, flexible and show comparable performance against other state-of-the-art hashing methods.
Deep Learning-Based Classification of Protein Subcellular Localization from Immunohistochemistry Images

Due to the recent breakthrough of bioimaging, automated classification of protein subcellular localization based on immunohistochemistry (IHC) images has become an important topic of proteomics research. Inspired by the impressive performance of deep learning in various image classifications, we trained a deep neural network model to classify protein images of eight subcellular localizations, which is able to achieve higher classification accuracies than using traditional models of support vector machine. Intermediate outputs of the neural network were visualized to show that our model can capture subtle texture features from IHC images and lead to better subcellular location classification results. In addition, our results show that data rebalance can significantly improve the classification performance in this multi-class deep classifier
Organizational Green Motivations for Information Technology: Empirical Study

This paper investigates the influence of organizational eco-sustainability motivations on the adoption of Green Information Technology (IT) and IT for Green. Drawing from the motivational theory and using data collected from a survey of 176 organizations, the paper develops and tests four hypotheses. The result shows that eco-efficiency and eco-effectiveness motives influence the adoption of (a) technologies that improve the energy efficiency of IT infrastructure and that reduce IT related emissions (b) information systems to reduce travel and travel related emissions and (c) policy and practices that cultivate product stewardship focusing on the IT lifecycle from procurement to end of life. The findings show that while a sense of corporate environmental responsibility predicts Green IT policies and IT for Green technologies, cost cutting and energy conservation are main reasons for undertaking Green IT investments. Pressure from market forces will emerge as relevant motive as early adopters demonstrate favorable outcome out of their green practices.
An Evaluation of the Implementation of a Court Outreach Program for People With Severe and Persistent Mental Illness Who Are Legally Involved

This paper describes an implementation evaluation of a Court Outreach Program that provides outreach and community-based treatment and support to people with mental illness in the legal system. The paper describes the Court Outreach Program and presents evaluation findings. Program strengths included competent and knowledgeable court outreach staff who contribute to the legal and mental health systems and provide access for clients to a range of community supports and treatments. Challenges included a limited program capacity to serve all potential clients and bottlenecks in the intake/assessment process. Recommendations are made to improve the implementation of the program.
Fast Genre Classification of Web Images Using Global and Local Features

A number of images are present on the Web and the number is increasing every day. To effectively mine the contents embedded in Web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis, such as text and non-text image discrimination. We herein propose a hierarchical algorithm for efficiently classifying Web images into four classes, namely, natural scene images, born-digital images, scanned and cameracaptured paper documents, which are the most prevalent image types on the Web. Our algorithm consists of two stages; the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first-stage classifier is processed by the second stage, which further extracts local texture features represented in the Bag-of-Words framework and uses another SVM classifier for final classification. In addition, we design two fusion strategies to train the second classifier and generate the final prediction label depending on the usage of local features in the second stage. To validate the effectiveness of our proposed method, we also build a database containing more than 55,000 images from various sources. On our test image set, we obtained an overall classification accuracy of 98.4% and the processing speed is over 27FPS on an Intel(R) Xeon(R) CPU (2.90GHz).
Holistic Handwritten Uyghur Word Recognition Using Convolutional Neural Networks

This paper presents an approach for holistic handwritten Uyghur word recognition using convolutional neural networks (CNNs). For a large number of word classes, it is hard to collect sufficient samples for each class. To overcome the insufficient training samples, we propose data augmentation techniques to increase samples by stroke deformation and whole shape rotation. The CNN has 8 convolutional layers for feature extraction and one full connection layer for classification. We evaluated the performance on a dataset of online handwritten Uyghur words with 2344 classes and obtained recognition accuracies over 99% on the test set. The performance is superior to those of handwritten Uyghur word recognition reported in the literature. Our results demonstrate that CNN is useful for holistic word recognition with large number of word classes.
Harnessing U-disparity in Point Clouds for Obstacle Detection

Lidar is an indispensable equipment in autonomous vehicle for environment perception, in which obstacle detection plays an important role in collision avoiding and route planning. The main challenge of Lidar based obstacle detection is that processing the disordered and sparse point clouds would be difficult and time-consuming. Hence, this paper presents a novel usage of U-disparity to locate obstacles indiscriminately with point clouds, which makes obstacle detection effective and efficient. The proposed method firstly uses cross-calibration to align point cloud with reference image, so that a depth map is formed. Then, the U-disparity map is introduced to process Lidar based depth map. Due to the particularity of Lidar based U-disparity, we select local peaks in column in U-disparity to identify relevant disparities of obstacles. After applying filtering and clustering steps on these salient peak disparities, the corresponding obstacles can be precisely localized. Quantitative and qualitative experimental results on KITTI object detection benchmark and road detection benchmark reveal that the proposed method achieves very encouraging performances in various environments.
Writer Identification in Indic Scripts: A Stroke Distribution Based Approach

This paper proposes to represent an offline handwritten document with a distribution of strokes over an alphabet of strokes for writer identification. A data driven approach for stroke alphabet creation is done as follows: strokes are extracted from the image, using a regression method, extracted strokes are represented as fixed length vectors in a vector space, strokes are clustered into stroke categories to create a stroke alphabet. The paper proposes a clustering method with a new clustering score whereby an optimal number of clusters (categories) are automatically identified. For a given document, based on the frequency of occurrence of elements in the stroke alphabet, a histogram is created that represents the writer's writing style. Support Vector Machine is used for the classification purpose. Offline handwritten documents written in two different Indic languages, viz., Telugu and Kannada, were considered for the experimentation. Results comparable to other methods in the literature are obtained from the proposed method.
Object Tracking by Branched Correlation Filters and Particle Filter

Correlation filter based tracking which is one of the tracking-by-detection methods, has demonstrated competitive performance in recent years. However, once the detected position is incorrect, the parameter of correlation filter is updated based on the appearance of the incorrect position. This causes the target loss, which is a crucial problem in a single object tracking task. In this paper, we propose a novel object tracking method that prevents target loss. In the proposed method, the object is detected using branched correlation filters, and each correlation filter corresponds to a candidate detection position. The optimal correlation filter is estimated by particle filter. The experiments confirmed that the AUC score of proposed method is 2.24 higher than that of the state-of-the-art method.
A News-Driven Recurrent Neural Network for Market Volatility Prediction

Extracting hidden information embedded in the financial news is an effective approach to market volatility prediction. In this paper, we propose a recurrent neural network (RNN) based method that dynamically extracts latent structures from the sequence of news events for market prediction. Specifically, we first train a skip-thought model on the financial news datasets to represent the semantic meaning of sentences. Then we aggregate the representations from a single day to form a daily feature to align with the market index. Finally, to make use of the news released some days ago for a better prediction, we exploit the long short-term memory RNN (LSTM-RNN) to integrate the information by exploring the dynamic patterns embedded in the sequence of news events. Extensive experiments on the public available Reuters and Bloomberg financial news datasets verified the effectiveness of our method and demonstrated that our method achieves the state-of-the-art performance.
Integrating Bidirectional LSTM with Inception for Text Classification

A novel neural network architecture, BLSTM-Inception v1, is proposed for text classification. It mainly consists of the BLSTM-Inception module, which has two parts, and a global max pooling layer. In the first part, forward and backward sequences of hidden states of BLSTM are concatenated as double channels, rather than added as single channel. The second part contains parallel asymmetric convolutions of different scales to extract nonlinear features of multi-granular n-gram phrases from double channels. The global max pooling is used to convert variable-length text into a fixed-length vector. The proposed architecture achieves excellent results on four text classification tasks, including sentiment classifications, subjectivity classification, and especially improves nearly 1.5% on sentence polarity dataset from Pang and Lee compared to BLSTM-2DCNN.
Detection of Road Vanishing Point by Stereo Camera

This paper proposes a novel road vanishing point detection method from a stereo pair of images. First, the improved v-disparity map is generated by sampling the flat region in the u-disparity map. Then the horizon and the approximate road region are extracted by a robust weighted-sampled RANSAC-like method in the improved v-disparity map. With the horizon and road-region constraints, the candidate region of vanishing point can be determined to reduce the complexity of voting. The vanishing point is located at the position with the largest voting score in the candidate region. The proposed approach has been tested on 2621 grayscale image pairs of different road scenes from the KITTI dataset. Our experiments demonstrate that this approach detects horizons and vanishing points at the state-of-the-art performance.
Improving Small Object Detection

While the problem of detecting generic objects in natural scene images has been the subject of research for a long time, the problem of detection of small objects has been largely ignored. While generic object detectors perform well on medium and large sized objects, they perform poorly for the overall task of recognition of small objects. This is because of the low resolution and simple shape of most small objects. In this work, we suggest a simple yet effective upsampling-based technique that performs better than the current state-of-the-art for end-to-end small object detection. Like most recent methods, we generate proposals and then classify them. We suggest improvements to both these steps for the case of small objects.
Conic Fitting: New Easy Geometric Method and Revisiting Sampson Distance

Fitting conic from images is a preliminary step for its plentiful applications. It's a common sense that geometric distance based fitting methods are better than algebraic distance based ones. However, for a long time, there has not been a geometric distance between a data point and a general conic that allows easy computation and achieves high accuracy simultaneously. In this paper, we derive a new geometric distance between a data point and a conic by revisiting Sampson distance. The new geometric distance is accurate and simultaneously still explicit analytical representation, which is greatly easy to be implemented. Then, based on the distance, a new cost function with combining Sampson distance is constructed. The conic fitting optimization by minimizing this cost function has all the merits of the geometric distance based methods and simultaneously avoids their limitations.
Estimating Food Calories for Multiple-Dish Food Photos

A food photo generally includes several kinds of food dishes. In order to recognize food images including multiple dishes, we need to detect each dish in food images. Meanwhile, in recent years, the accuracy of object detection has improved drastically by the appearance of CNN. In this paper, we apply Faster R-CNN [10], a major object detection method, to food photos of multiple dishes. In the experiments we verify by using two kinds of food photo datasets. In addition, this food detector is applied to food calorie estimation for food photos of multiple dishes. We use Faster R-CNN as a food detector to detect each dish in a food image, and the food calorie of each detected dish are estimated by image-based food calorie estimation [2]. In this way, we estimate food calories from a food photo of multiple dishes. In this experiment, we collect food photos of multiple dishes with total food calorie of multiple dishes. Then we estimate food calories from food photos of multiple dishes by combining the food detector and image based food calorie estimation.
Enhanced Adaptive Locality Preserving Projections for Face Recognition

In this paper, we address the graph-based manifold learning method for face recognition. The proposed method is called enhanced adaptive Locality Preserving Projections. The EALPP integrates four properties: (i) introduction of data label information and parameterless computation of affinity matrix, (ii) QR-decomposition for acceleration of the eigenvector computation, (iii) matrix exponential for solving the problem of singular matrix and (iv) processing of uncorrelated vector of projection matrix. EALPP has been integrated two techniques: Maximum Margin Criterion (MMC) and Locality Preserving Projections (LPP). Face recognition test on four public face databases (ORL, Yale, AR and UMIST) and experimental results demonstrate the effectiveness of EALPP.