Learning on Graphs with Partially Absorbing Random Walks: Theory and Practice

Learning on graphs has been studied for decades with abundant models proposed, yet many of their behaviors and relations remain unclear. This thesis fills this gap by introducing a novel second-order Markov chain, called partially absorbing random walks (ParWalk). Different from ordinary random walk, ParWalk is absorbed at the current state $i$ with probability $p_i$, and follows a random edge out with probability $1-p_i$. The partial absorption results in absorption probability between any two vertices, which turns out to encompass various popular models including PageRank, hitting times, label propagation, and regularized Laplacian kernels. The unified treatment reveals the distinguishing characteristics of these models arising from different contexts, and allows comparing them and transferring findings from one paradigm to another. The key for learning on graphs is capitalizing on the cluster structure of the underlying graph. The absorption probabilities of ParWalk, turn out to be highly effective in capturing the cluster structure. Given a query vertex $q$ in a cluster $\mathcal{S}$, we show that when the absorbing capacity ($p_i$) of each vertex on the graph is small, the probabilities of ParWalk to be absorbed at $q$ have small variations in region of high conductance (within clusters), but have large gaps in region of low conductance (between clusters). And the less absorbent the vertices of $\mathcal{S}$ are, the better the absorption probabilities can represent the local cluster $\mathcal{S}$. Our theory induces principles for designing reliable similarity measures and provides justification to a number of popular ones such as hitting times and the pseudo-inverse of graph Laplacian. Furthermore, it reveals their new important properties. For example, we are the first to show that hitting times is better in retrieving sparse clusters, while the pseudo-inverse of graph Laplacian is better for dense ones. The theoretical insights instilled from ParWalk guide us in developing robust algorithms for various applications including local clustering, semi-supervised learning, and ranking. For local clustering, we propose a new method for salient object segmentation. By taking a noisy saliency map as the probability distribution of query vertices, we compute the absorption probabilities of ParWalk to the queries, producing a high-quality refined saliency map where the objects can be easily segmented. For semi-supervised learning, we propose a new algorithm for label propagation. The algorithm is justified by our theoretical analysis and guaranteed to be superior than many existing ones. For ranking, we design a new similarity measure using ParWalk, which combines the strengths of both hitting times and the pseudo-inverse of graph Laplacian. The hybrid similarity measure can well adapt to complex data of diverse density, thus performs superiorly overall. For all these learning tasks, our methods achieve substantial improvements over the state-of-the-art on extensive benchmark datasets

Recognition of speed signs in uncertain and dynamic environments

The speed limit signs recognition directly affects the safety of autonomous vehicles. Vehicles are usually running in an uncertain and dynamic environment. The performance of the recognition system is affected by various factors such as the different sizes of pictures, illumination condition and position circumstances, which can lead to misclassification. This makes the speed sign recognition challengeable. To improve the recognition rate of the speed signs in such environments, this work firstly applies the method of the saliency target detection based on the background-absorbing Markov chain, to extract the node in an image, then uses SPP-CNN to classify the extracted nodes with ten-folder validation. The recognition rate is up to 9.32%, higher than that obtained directly by SPP-CNN working on raw dataset

Advanced Visual Computing for Image Saliency Detection

Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks

Salient Object Detection Techniques in Computer Vision-A Survey.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end