{'fraction_non_alphanumeric': 0.02171767028627838, 'fraction_numerical': 0.005923000987166831, 'mean_word_length': 5.417721518987341, '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}}
Semantic scene detection system for baseball videos based on the MPEG-7 specification

In this paper, we proposed a content-based multimedia analysis/retrieval system mainly based on the MPEG-7 specification which is capable of handling the high-level content analysis such as the semantic scene detection for baseball broadcast videos. Here, eight semantic scene classes were predefined for baseball videos. First, an effective shot boundary detection scheme based on scalable color histogram was proposed to segment a video into many shots. Then, various visual features including field color features, skin color features, and camera motion information were extracted to analyze the semantics for each shot. According to the visual properties of different scenes, we developed a two-stage classification strategy for the semantic scene detection. Finally, the experimental results showed that the proposed framework identifies eight semantic baseball scenes with 81% of precision rates and 84% of recall rates.