Salient object detection via reliable boundary seeds and saliency refinement

Abstract

Salient object detection can identify the most distinctive objects in a scene. In this study, a novel graph-based approach is proposed to detect a salient object via reliable boundary seeds and saliency refinement. A natural image is firstly mapped to a graph with superpixels as nodes. Saliency information is then diffused over the graph using seeds. For the reason that the boundary nodes may contain salient nodes, it is not appropriate to use all boundary nodes as the background seeds. Therefore, a boundary saliency measurement is proposed to obtain more accurate background seeds. After that, the information of background seeds is diffused by a two-stage scheme. A background-based map and a foreground-based map are generated based on the two-stage scheme. Furthermore, in order to enhance the detection accuracy, a refinement model is presented to fuse the information of background-based and foreground-based maps. Experiments on seven public datasets show the proposed algorithm out-performs the state-of-the-art salient object detection algorithms.

Basketball Object Extraction Method Based on Image Segmentation Algorithm

“…For example, color and texture features are the basis for classifying pixels on local images; color and texture are commonly used for pixel classification; meanwhile, contour and shape features are the basis for classifying regions in the image [19]. In addition, according to the preset, the local characteristics between pixels classified into different categories will also be similar; for example, the water surface and the sky are blue; they have the same color characteristics; at the same time, noise easily affects the local features [20,21].…”

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”