NDLI: Multiple Bernoulli relevance models for image and video annotation

Content Provider	IEEE Xplore Digital Library
Author	Feng, S.L. Manmatha, R. Lavrenko, V.
Copyright Year	2004
Description	Author affiliation: Multimedia Indexing & Retrieval Group, Massachusetts Univ., Amherst, MA, USA (Feng, S.L.; Manmatha, R.; Lavrenko, V.)
Abstract	Retrieving images in response to textual queries requires some knowledge of the semantics of the picture. Here, we show how we can do both automatic image annotation and retrieval (using one word queries) from images and videos using a multiple Bernoulli relevance model. The model assumes that a training set of images or videos along with keyword annotations is provided. Multiple keywords are provided for an image and the specific correspondence between a keyword and an image is not provided. Each image is partitioned into a set of rectangular regions and a real-valued feature vector is computed over these regions. The relevance model is a joint probability distribution of the word annotations and the image feature vectors and is computed using the training set. The word probabilities are estimated using a multiple Bernoulli model and the image feature probabilities using a non-parametric kernel density estimate. The model is then used to annotate images in a test set. We show experiments on both images from a standard Corel data set and a set of video key frames from NIST's video tree. Comparative experiments show that the model performs better than a model based on estimating word probabilities using the popular multinomial distribution. The results also show that our model significantly outperforms previously reported results on the task of image and video annotation.
Sponsorship	IEEE Comput. Soc
File Size	566693
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image retrieval Information retrieval Testing Kernel Indexing Probability distribution Distributed computing Image databases Search engines Content based retrieval
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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