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

Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features

Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection. Although, depth information can help improve detection results, the exploration of CNNs for RGB-D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB-D salient object detection that exploits high-level, mid-level, and low level features. Further, we present novel depth features that capture the ideas of background enclosure and depth contrast that are suitable for a learned approach. We show improved results compared to state-of-the-art RGB-D salient object detection methods. We also show that the low-level and mid-level depth features both contribute to improvements in the results. Especially, F-Score of our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second place

Are All Pixels Equally Important? Towards Multi-Level Salient Object Detection

When we look at our environment, we primarily pay attention to visually distinctive objects. We refer to these objects as visually important or salient. Our visual system dedicates most of its processing resources to analyzing these salient objects. An analogous resource allocation can be performed in computer vision, where a salient object detector identifies objects of interest as a pre-processing step. In the literature, salient object detection is considered as a foreground-background segmentation problem. This approach assumes that there is no variation in object importance. Only the most salient object(s) are detected as foreground. In this thesis, we challenge this conventional methodology of salient-object detection and introduce multi-level object saliency. In other words, all pixels are not equally important. The well-known salient-object ground-truth datasets contain images with single objects and thus are not suited to evaluate the varying importance of objects. In contrast, many natural images have multiple objects. The saliency levels of these objects depend on two key factors. First, the duration of eye fixation is longer for visually and semantically informative image regions. Therefore, a difference in fixation duration should reflect a variation in object importance. Second, visual perception is subjective; hence the saliency of an object should be measured by averaging the perception of a group of people. In other words, objective saliency can be considered as the collective human attention. In order to better represent natural images and to measure the saliency levels of objects, we thus collect new images containing multiple objects and create a Comprehensive Object Saliency (COS) dataset. We provide ground truth multi-level salient object maps via eye-tracking and crowd-sourcing experiments. We then propose three salient-object detectors. Our first technique is based on multi-scale linear filtering and can detect salient objects of various sizes. The second method uses a bilateral-filtering approach and is capable of producing uniform object saliency values. Our third method employs image segmentation and machine learning and is robust against image noise and texture. This segmentation-based method performs the best on the existing datasets compared to our other methods and the state-of-the-art methods. The state-of-the-art salient-object detectors are not designed to assess the relative importance of objects and to provide multi-level saliency values. We thus introduce an Object-Awareness Model (OAM) that estimates the saliency levels of objects by using their position and size information. We then modify and extend our segmentation-based salient-object detector with the OAM and propose a Comprehensive Salient Object Detection (CSD) method that is capable of performing multi-level salient-object detection. We show that the CSD method significantly outperforms the state-of-the-art methods on the COS dataset. We use our salient-object detectors as a pre-processing step in three applications. First, we show that multi-level salient-object detection provides more relevant semantic image tags compared to conventional salient-object detection. Second, we employ our salient-object detector to detect salient objects in videos in real time. Third, we use multi-level object-saliency values in context-aware image compression and obtain perceptually better compression compared to standard JPEG with the same file size