Local Saliency Detection Research Articles

The fusion of infrared and visible images via decomposition-based structure transfer and local saliency detection

Most of existing image fusion methods neglect the differences between multi-source images and extract characteristics without any discrimination, which negatively impacts the fused result, particularly in infrared (IR) and visible image fusion. This paper proposes a two-scale fusion method using total variation (TV) model with structure transfer and local saliency detection. Firstly, we use rolling guidance filter (RGF) to perform two-scale decomposition on the input images. Secondly, we combine the base layers with TV minimization based on structure transfer. Then we construct the weight map with local saliency detection and guide filter, and combine the detail layers by weight average. At last, the fused result is reconstructed with the fused base layer and the fused detail layer. The fusion result retains more target location and appearance information, which is beneficial to subsequent processing. The comparisons with 7 other methods reveal that our method achieves a superior performance.

Boundary Preserved Salient Object Detection Using Guided Filter Based Hybridization Approach of Transformation and Spatial Domain Analysis

Techniques for detecting salient objects mimic human behavior by recognizing the most noticeable parts of images as objects. Salient object detection has attracted many researchers’ attention nowadays for various computer vision and pattern recognition applications. In this paper, a unique approach is proposed based on the global and local saliency detection using wavelet transform and hybridizing it with learning-based saliency detection using a guided filter. First, the input image is subjected to superpixel segmentation to achieve visually uniform regions and to reduce the computational cost. The global and local saliency maps are then generated using global and local features extracted by the wavelet transform of the segmented image, as the wavelet transform gives a multiscale analysis of images in frequency as well as in spatial domain. The learning-based saliency maps are generated using random forest regression which considers the location, color, and textural features of the segmented image. The global and local saliency maps are fused to generate the wavelet-based saliency map which is further hybridized with the saliency map generated using random forest regression. The paper discusses the novel technique for hybridizing wavelet-based and learning-based saliency maps using a guided filter-based attention map generation. Several experiments are conducted on five different saliency datasets containing images with complex backgrounds, multiple objects, and low contrast. To evaluate the efficacy of the proposed method, extensive qualitative and quantitative performance analysis is carried out. Experimental results validate the significant improvement in the detection of salient regions as compared to the state-of-the-art methods.

A novel method for vehicle headlights detection using salient region segmentation and PHOG feature

In this paper, we explore an issue that is to detect vehicle headlights from the nighttime traffic surveillance images with highly reflections. In the night, reflections on the road (water) surface, vehicles bodies, or some reflective objects (such as lane markings, traffic signs) will interfere the headlight detection seriously. Although the existing methods have achieved good results, however, most of them failed to detect the headlight when headlights are far from camera. In order to solve the issue, we propose a novel method for vehicle headlights detection. The proposed method makes full use of the brightness and gradient information of the headlights in the night. First, we propose an effective region-of-interest (ROI) segmentation method which is based on multi-scale local saliency detection. The method pre-serve faint or small-sized objects and retain the original shape of the object to the greatest extent. Then, we compute the pyramid histogram of oriented gradients (PHOG) features, which are used to train support vector machine (SVM) classifier. Finally, the extracted bright blocks are classified according to the pre-trained SVM classifier. Experimental results and quantitative evaluations in different scenes demonstrate that our proposed method can achieve a better result compared with previous methods.

Infrared & visible images fusion based on redundant directional lifting-based wavelet and saliency detection

This paper proposes a novel infrared and visible image fusion algorithm, based on redundant directional lifting-based wavelet (RDL-Wavelet) and a hybrid saliency detection algorithm. Firstly, the input images are decomposed into approximation coefficients matrix and detail coefficient matrices. Comparing to traditional lifting-based wavelet, the proposed method involves no subsample processes, hence Gibbs artificialities can be alleviated with its shift-invariant property. Furthermore, it can adapt far better to the image orientation features in which predicting and updating signals can be derived even at the fractional pixel precision level, with the help of ‘Sinc’ interpolation technique. Secondly, a fusion rule based on saliency detection, which concerning both local contrast and global contrast informations, is adopted to obtain the result’s approximation and detail coefficients matrices. In local saliency detection process, morphology close operation is used to generate the refined saliency map. Finally, inverse RDL-Wavelet serves to regain fused image. Meanwhile, a feasible optimization scheme can be used to further enhance the computing efficiency of proposed algorithm. Experiments show that our method gives improved quantitative and qualitative results compared with recently developed methods.

A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory

Saliency detection is one of the most valuable research topics in computer vision. It focuses on the detection of the most significant objects/regions in images and reduces the computational time cost of getting the desired information from salient regions. Local saliency detection or common pattern discovery schemes were actively used by the researchers to overcome the saliency detection problems. In this paper, we propose a bottom-up saliency fusion method by taking into consideration the importance of the DS-Evidence (Dempster–Shafer (DS)) theory. Firstly, we calculate saliency maps from different algorithms based on the pixels-level, patches-level and region-level methods. Secondly, we fuse the pixels based on the foreground and background information under the framework of DS-Evidence theory (evidence theory allows one to combine evidence from different sources and arrive at a degree of belief that takes into account all the available evidence). The development inclination of image saliency detection through DS-Evidence theory gives us better results for saliency prediction. Experiments are conducted on the publicly available four different datasets (MSRA, ECSSD, DUT-OMRON and PASCAL-S). Our saliency detection method performs well and shows prominent results as compared to the state-of-the-art algorithms.

Discovering Primary Objects in Videos by Saliency Fusion and Iterative Appearance Estimation

In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual/motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts.

LGOH-Based Discriminant Centre-Surround Saliency Detection

Discriminant saliency is a kind of decision-theoretic-based saliency detection method that has been proposed recently. Based on local gradient distribution, this paper proposes a simple but efficient discriminant centre-surround hypothesis, and builds local and global saliency models by combining multi-scale intensity contrast with colour and orientation features. This method makes three important contributions. First, a circular and multi-scale hierarchical centre-surround profile is designed for the local saliency detection. Secondly, the dense local gradient orientation histogram (LGOH) of the centre-surround region is counted and used for the local saliency analysis. And thirdly, a new integration strategy for the local and global saliency is proposed and applied to the final visual saliency discriminant. Experiments demonstrate the effectiveness of the proposed method. Compared with 12 state-of-the-art saliency detection models, the proposed method outperforms the others in precision-recall, F-measures and mean absolute error (MAE), and can produce a more complete salient object.