Informatics Report Series
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Title:Automatic Image Annotation in Retrieval using Cross-Media Relevance Models |
Authors:
J Jeon
; Victor Lavrenko
; R Manmatha
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Date:Jul 2003 |
Publication Title:Proceedings of the 26th annual international ACM SIGAR conference on Research and development in information retrieval |
Publication Type:Conference Paper
Publication Status:Published
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Page Nos:119-126
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DOI:10.1145/860435.860459
ISBN/ISSN:1-58113-646-3
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- Abstract:
- Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labor intensive procedure and hence there has been great interest in coming up with automatic ways to retrieve images based on content. Here, we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, we show that probabilistic models allow us to predict the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is almost six times as good (in terms of mean precision) than a model based on word-blob co-occurrence model and twice as good as a state of the art model derived from machine translation. Our approach shows the usefulness of using formal information retrieval models for the task of image annotation and retrieval.
- Links To Paper
- No links available
- Bibtex format
- @InProceedings{EDI-INF-RR-1191,
- author = {
J Jeon
and Victor Lavrenko
and R Manmatha
},
- title = {Automatic Image Annotation in Retrieval using Cross-Media Relevance Models},
- book title = {Proceedings of the 26th annual international ACM SIGAR conference on Research and development in information retrieval},
- year = 2003,
- month = {Jul},
- pages = {119-126},
- doi = {10.1145/860435.860459},
- }
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