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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(More)
We apply a continuous relevance model (CRM) to the problem of directly retrieving the visual content of videos using text queries. The model computes a joint probability model for image features and words using a training set of annotated images. The model may then be used to annotate unseen test images. The probabilistic annotations are used for retrieval(More)
A number of projects are creating searchable digital libraries of printed books. These include the Million Book Project, the Google Book project and similar efforts from Yahoo and Microsoft. Content-based on line book retrieval usually requires first converting printed text into machine readable (e.g. ASCII) text using an optical character recognition (OCR)(More)
Libraries contain enormous amounts of handwritten historical documents which cannot be made available on-line because they do not have a searchable index. The wordspotting idea has previously been proposed as a solution to creating indexes for such documents and collections by matching word images. In this paper we present an algorithm which compares whole(More)
In this paper we describe a novel approach for jointly modeling the text and the visual components of multimedia documents for the purpose of information retrieval(IR). We propose a novel framework where individual components are developed to model different relationships between documents and queries and then combined into a joint retrieval framework. In(More)
This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histogram-of-gradients features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character(More)
In this paper we explore different approaches for improving the performance of dependency models on discrete features for handwriting recognition. Hidden Markov models have often been used for handwriting recognition. Conditional random fields (CRF's) allow for more general dependencies and we investigate their use. We believe that this is the first attempt(More)