关键词: 机器学习, 自然语言处理, 特征融合, Sentence-Rank, [N]-gram

Abstract: In the traditional image semantic sentence annotation, sentence templates are used to describe the content of image. However, it is hard to meet the logic of language using the traditional method. Aiming at this problem, this paper proposes to describe the image content by selecting an optimal sentence from the corpus , and design the Sentence-Rank algorithm with the [N]-gram algorithm to generate the annotated sentence. Firstly, the HSV-LBP-HOG fusion feature with the best performance is used for image classification, the image markings are obtained. Then, it uses the string matching algorithm to list all the sentences with the marked keywords from the corpus and sorts the obtained sentences by Sentence-Rank algorithm, and selects the highest rated sentence to describe the image. The experimental results show that the annotation sentence obtained by this method has lower perplexity, and solves the linguistic logic problem of sentences better.

Key words: machine learning, natural language processing, feature fusion, Sentence-Rank, [N]-gram