# Salient Object Detection with Convex Hull Overlap Source: [Salient Object Detection with Convex Hull Overlap]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:38:59.898Z Authors: Yongqing Liang Published: 2016-12-10T12:42:10.000Z ## Abstract Salient object detection plays an important part in a vision system to detect important regions. Convolutional neural network (CNN) based methods directly train their models with large-scale datasets, but what is the crucial feature for saliency is still a problem. In this paper, we establish a novel bottom-up feature named convex hull overlap (CHO), combining with appearance contrast features, to detect salient objects. CHO feature is a kind of enhanced Gestalt cue. Psychologists believe that surroundedness reflects objects overlap relationship. An object which is on the top of the others is attractive. Our method significantly differs from other earlier works in (1) We set up a hand-crafted feature to detect salient object that our model does not need to be trained by large-scale datasets; (2) Previous works only focus on appearance features, while our CHO feature makes up the gap between the spatial object covering and the object's saliency. Our experiments on a large number of public datasets have obtained very positive results. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()