We present an unsupervised approach to automated story picturing. Semantic keywords are extracted from the story, an annotated image database is searched. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content play a role in determining the ranks. Annotations are processed using the Wordnet. A mutual reinforcement-based rank is calculated for each image. We have implemented the methods in our Story Picturing Engine (SPE) system. Experiments on large-scale image databases are reported. A user study has been performed and statistical analysis of the results has been presented.
▪ Abstract Fossil fuels account for about 80% of energy consumption in Asia. Because of its abundance and easy recoverability, especially in India and China, coal will remain the fuel of choice in the foreseeable future. If current trends continue, sulfur dioxide emissions from Asia may soon equal the emissions from North America and Europe combined. These trends portend a variety of local, regional, and global environmental impacts. Acid rain damages human health, ecosystems, and built surfaces. Many ecosystems will be unable to absorb these increased acidic depositions, leading to irreversible ecosystem damage with far-reaching implications for health, forestry, agriculture, fisheries, and tourism. RAINS-ASIA is a scenario-generating tool used to estimate the extent of damages caused by acid rain and to review the costs and impacts of alternatives to provide a look into the future. Its use extends from national-, regional-, and city-scale evaluation and inputs for cost-effective options analyses, to international negotiations on transboundary pollution.
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