| Summary: | 碩士 === 國立高雄應用科技大學 === 資訊工程系 === 105 === One of the difficulties in searching natural images is the semantic gap between low-level pixel data and the content that the image is perceived by human. Most of the research focus on entity images which often contain objects in it, such as dinosaurs, flowers and buses. On the contrary, to search for nonobjective images, for example, art, emotion, friendship, time and freeze, is a much more difficult challenge than typical entity images retrieval. Because nonobjective images can only be judged by the surrounding environment, feelings or subjective understandings and they usually do not have clear or consistent objects in it. Thus, we first manually classified selected images into the five categories aforementioned. Then we built SVMs to train and test on these pre-classified nonobjective images. Finally, the results were compared with common retrieval methods. We hope that our approach and results of this thesis will contribute to the identification and analysis of nonobjective images, and bring the semantic gap closer in the future.
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