Multiple Bernoulli Relevance Models for Image and Video Annotation

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 experim...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Shaolei Feng, Raghavan Manmatha, Victor Lavrenko
• Keywords: Automatic Image Annotation, Bernoulli Relevance Model, Computer Vision, CVPR 2004, Image Feature Probabilities, Image Feature Vectors, Multiple Bernoulli Model