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

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