{'fraction_non_alphanumeric': 0.027315914489311165, 'fraction_numerical': 0.0035629453681710215, 'mean_word_length': 5.153284671532846, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Saliency Model for Stereoscopic Images

In this paper, we propose a novel saliency model for stereoscopic images. To improve depth information for stereo saliency analysis, this model exploits depth information from three aspects: 1) we extract the low-level features based on the color-depth contrast features in a local and global search range (local-global contrast); 2) to extract the topological structural from a depth map, a surrounding map based on a Boolean map is obtained as a weight value to enhance the local-global contrast features; and 3) based on the saliency probability distribution in depth information, we employ stereo center prior enhancement to compute the final saliency. Experimental results on two recent eye-tracking databases show that our proposed method outperforms the state-of-the-art saliency models.