Automatic image annotation approach using visual word frequency and semantic information

• Main Authors: Yi-Zhen Li, 李懿真
• Other Authors: Shih-Chieh Chou
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/sh2834

碩士 === 國立中央大學 === 資訊管理學系 === 106 === In common image searching scenarios, Image Search Engines like Google Image and Flickr that most people using usually are built on Text-Based Image Retrieval techniques. By searching with keywords that user provide, Text-Based Image Retrieval techniques extremely rely on the describing context tag on images within the database. However, the practical data image uploader seldom provides detailed image tags or context description that make it even harder for Text-Based Image Retrieval to identify the correct image. To solve this problem, the development of Automatic Image Annotation is aimed to improve the process of manual construction. How to effectively accomplish image retrieval and management has become a popular research topic in IT field since massive image data are now available in digital era. We propose an Automatic Image annotation approach integrating visual words and semantic words. Using popular image retrieval method Bag-of-Visual-Words to extract image features and combining with TF-IDF to calculate weighted visual word’s frequency, we can identify the most representative visual words for image. Furthermore, we apply Word2Vec model to conceptualize the meaning of context and generate image tags with proper semantic meaning. In this study, we use multi-label outdoor image dataset LabelMe to perform model training and experiments and discuss about the practicability and efficiency of this approach via Precision Rate, Recall Rate, and F1-measure.