Title
Image Recommendation for Automatic Report Generation using Semantic Similarity
Abstract
Automatic report generation is a technology that automatically generates documents in the form of report by summarizing various materials according to a specific topic in time sequence or subject. Although the main content of the report is text, insertion of appropriate images can improve the completeness of the report. In this paper, we propose an image recommendation method for automatically selecting and inserting appropriate images corresponding to a specific part of a report. In our proposed method, reevaluation of the candidate images is performed based on the semantic similarity between query and the contents of the images. In order to transform semantic information of text query and image into one vector space, we extracted semantic information from image as a set of tags form using deep learning based object detection module. Also, we extracted tags from the given title of the image so that the proposed system can evaluate the candidate images even in the case that the given query includes specific keywords or proper nouns which were not learned by object detection and recognition module in advance. In this paper, we conducted experiments on eight queries related to recent events to verify the applicability of our proposed image recommendation system and evaluate the image selection accuracy.
Year
DOI
Venue
2019
10.1109/ICAIIC.2019.8669018
2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Keywords
Field
DocType
Semantics,Computer science,Object detection,Transforms,Data mining,Deep learning,Image recognition
Recommender system,Semantic similarity,Object detection,Vector space,Information retrieval,Computer science,Artificial intelligence,Deep learning,Completeness (statistics),Proper noun,Semantics
Conference
ISBN
Citations 
PageRank 
978-1-5386-7822-0
2
0.58
References 
Authors
0
2
Name
Order
Citations
PageRank
Changhun Hyun121.25
Hyeyoung Park219432.70