Thumbnail
Access Restriction
Open

Author Yavlinsky, Alexei ♦ Rüger, Stefan
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Realistic Retrieval Scenario ♦ New Image Concept ♦ Web Image Datasets ♦ Efficient Approach ♦ Keyword Combination ♦ Target Concept ♦ Getty Image Archive ♦ Automated Image Annotation ♦ Acceptable Performance Tradeoff ♦ Limited Vocabulary Size ♦ Test Collection ♦ Multiple Keyword Query ♦ Low Annotation Accuracy ♦ Image Index ♦ Traditional Annotation Model ♦ Initial Automated Annotation ♦ Small Computational Cost ♦ Efficient Re-indexing ♦ Appropriate Set ♦ Fast Image Re-indexing ♦ Retrieval Result ♦ Image Collection ♦ Example Image
Description This report presents a framework for improving the image index obtained by automated image annotation. Within this framework, the technique of keyword combination is used for fast image re-indexing based on initial automated annotations. It aims to tackle the challenges of limited vocabulary size and low annotation accuracies resulting from differences between training and test collections. It is useful for situations when these two problems are not anticipated at the time of annotation. We show that based on example images from the automatically annotated collection, it is often possible to find multiple keyword queries that can retrieve new image concepts which are not present in the training vocabulary, and improve retrieval results of those that are already present. We demonstrate that this can be done at a very small computational cost and at an acceptable performance tradeoff, compared to traditional annotation models. We present a simple, robust, and computationally efficient approach for finding an appropriate set of keywords for a given target concept. We report results on TRECVID 2005, Getty Image Archive, and Web image datasets, the last two of which were specifically constructed to support realistic retrieval scenarios. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2007-01-01
Publisher Institution In Proceedings of SPIE Volume 6506. Multimedia Content Access: Algorithms and Systems