Visual saliency refers to the physical

bottom-up distinctness of image details.It is a relative property that depends on the degree to which a pixel or region is visually distinct from its background.The region-based contrast method(RC) achieves good results on public dataset. However

the over-segmentation of the method is considered as a preview contrast computation which contributes to the high precision.In this study

a novel bottom-up salient object detection method based on color names and spatial information is proposed

in which regional contrast and spatial compactness are considered as two factors for saliency evaluation.In addition

we express the prior knowledge of traffic signs with top-down saliency maps

combined with the bottom-up saliency maps

to detect traffic signs from real world images.First

we learn color names offline by a probabilistic latent semantic analysis model to find the mapping between color names and pixel values.The color names can be used for image segmentation and region description.Second

pixels are assigned into special color names according to their values to form different color clusters in the image.The saliency measure for every color cluster is evaluated by its intra spatial variance.The less the color cluster spreads the more salient it is.Third

every color cluster is divided into some local regions which are represented by color name descriptors.The regional contrast is evaluated by computing color distance between different regions in entire image.Last

the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast.Note that in most cases

it is not the bottom-up saliency

but the most "interesting" object in an image that attracts attention.In this study

road signs are divided into three categories; every category has special color information.For each category

a class-specific distribution is constructed by the bag-of-words(BoW)model with training images to form the top-down saliency maps.Then the traffic signs are detected from the saliency maps

which are generated by combining the bottom-up saliency maps with top-down saliency maps.When evaluated using one of the largest publicly available datasets

our method outperforms several existing salient object detection methods with an achieved accuracy of 92%.The ROC curve generated by our method is better than the curves produced by other methods with the area under curve(AUC)of 0.9453.In addition

when tested on the traffic sign dataset constructed by ourselves

our method achieves a detection rate of 90.7%.In the paper

we propose a novel saliency detection method

in which an image is treated as a composition of 11 basic color names.Every pixel belongs to different color names in a probabilistic manner.Based on this idea

the image is divided into some color clusters

followed by segmenting every cluster into local regions.For saliency measure evaluation

both spatial compactness of the color cluster and the region contrast are considered.Our approach achieves the best results compared with some state-of-the-art methods on the public dataset.Furthermore

it obtains good performance when considering task-dependent application for traffic sign detection.