Markov Chain and Adaboost Image Saliency Detection Algorithm Based on Conditional Random Field

Abstract

The traditional salient object detection algorithms are used to apply the underlying features and prior knowledge of the images. Based on conditional random field Markov chain and Adaboost image saliency detection technology, a saliency detection method is proposed to effectively reduce the error caused by the target approaching the edge, which mainly includes the use of absorption Markov chain model to generate the initial saliency map. In this model, the transition probability of each node is defined by the difference of color and texture between each super pixel, and the absorption time of the transition node is calculated as the significant value of each super pixel. A strong classifier optimization model based on Adaboost iterative algorithm is designed.The initial saliency map is processed by the classifier to obtain an optimized saliency map, which highlights the global contrast. In order to extract the saliency region of the final saliency map, a method using conditional random field is designed to segment and extract the saliency region. The results show that the saliency area detected by this method is prominent, the overall contour is clear and has high resolution. At the same time, this method has better performance in accuracy recall curve and histogram.