In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

A review on neural networks with random weights

This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.

An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Although traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based traffic signs. This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labeled from street views. The post-processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.

Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network

As an important component of the driver assistance system or autonomous vehicle, traffic-sign recognition can provide drivers or vehicles with safety and alert information about the road. This paper proposes a new method for the task of traffic-sign recognition by employing extreme learning machine (ELM) whose infrastructure is a single-hidden-layer feed-forward network. This method includes two stages: One is the training stage which estimates the parameters of ELM based on training images of traffic signs; the other is the recognition stage which identifies each test image by using the trained ELM. Histogram-of-gradient descriptors are used as features in this proposed method. The German traffic sign recognition benchmark data set [1] with more than 50000 images of German road signs over 43 classes is used. Experimental results have shown that this proposed method achieves not only high recognition precision but also extremely low computational cost in terms of both training and recognition stages. An outstanding balance between recognition ratio and computational speed is obtained.

A novel method for traffic sign recognition based on extreme learning machine

This paper proposes a hierachical method for traffic sign detection by employing extreme learning machine (ELM) whose infrastructure is a single-hidden-layer feedforward network. This proposed method consists of three modules: Coarse detection module, fine detection module and candidates clustering module. Histogram of oriented gradient (HOG) and color histogram are used as features of signs. This proposed method is tested on German traffic sign detection benchmark (GTSDB) data set, which has more than 900 images of German road signs covering 43 classes. The architecture of this proposed method is simple and it has strong extensionality. Experimental results have shown that this proposed method achieves 98.60% in terms of area under curve (AUC) for all categories of traffic signs in the dataset.

Extreme learning machine based traffic sign detection

Visual object tracking in unconstrained environments is a challenging task in computer vision. How to design an efficient discriminative feature representation is one challenging issue. To improve the adaptability of the tracker to large object appearance changes, the observation model needs to be updated online. However, a bad model update using inaccurate training samples can lead to model drift problem. Therefore, how to design an efficient online observation model and a model update strategy are two other challenging issues. This paper proposes the concatenation of histogram of oriented gradients variant (HOGv) and color histogram as the feature representation to balance discriminative power and efficiency. The single-hidden-layer feedforward neural network (SFNN) is used as an observation model, and the recursive orthogonal least squares (ROLS) algorithm is used to update the model online. A bidirectional tracking scheme is designed to alleviate the model drift problem during online tracking. The proposed bidirectional tracking scheme consists of three modules: the forward tracking module, the backward tracking module and the integration module. The forward tracking module first finds all the candidate regions, and then, the backward tracking module calculates the respective confidence of each candidate region according to historical information. Finally, the integration module integrates both of the first two modules' results to determine the final tracked object and the model update strategy for the current frame. Extensive evaluations of the existing tracking benchmarks have shown that the proposed tracking framework results in significant performance improvements compared with the base tracker, and it outperforms most of the state-of-the-art trackers.

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Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Target tracking is a challenging task in computer vision. It aims to detect and track particular objects in sequences. Illumination variation, motion of target, occlusion and background clutter make target tracking extremely challenging. We propose an novel online target tracking method which based on extreme learning machine(ELM). This tracking method consists of three modules: training, tracking and classifier update. The training stage aims to train ELM by using the training set. Extracting histograms of oriented gradients (HOG) features in the first frame of each sequence for training ELM. Then the tracking stage will make predictions about the object position and detect the target in candidate regions. A simple object motion model is designed to predict the object position. Finally, according to tracking results, the classifier can be updated for online learning. A large number of experimental results have validated this proposed method.

Zhiyong Huang

Papers

An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

A novel method for traffic sign recognition based on extreme learning machine

Extreme learning machine based traffic sign detection

Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

An object tracking method using extreme learning machine with online learning