Salient Region: Presentations of Image Main Contents and its Exaction Algorithms

摘要

In order to accurately detect those salient useful regions with flat textures, we present a novel saliency metric coupled with color and texture features and its corresponding salient region extraction methods. Salient regions are usually defined as those regions that could present the main meaningful or semantic contents. However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics and this will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. In this study, three main colors and multi-resolution Gabor features are respectively used to evaluate the corresponding saliency values of implicit regions in one image. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.