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- Information Search and Retrieval (2)
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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
A Hidden Markov Model For Alphabet-Soup Word Recognition, Shaolei Feng, Nicholas Howe, R. Manmatha
A Hidden Markov Model For Alphabet-Soup Word Recognition, Shaolei Feng, Nicholas Howe, R. Manmatha
R. Manmatha
Recent work on the ``alphabet soup'' paradigm has demonstrated effective segmentation-free character-based recognition of cursive handwritten historical text documents. The approach first uses a joint boosting technique to detect potential characters - the alphabet soup. A second stage uses a dynamic programming algorithm to recover the correct sequence of characters. Despite experimental success, the ad hoc dynamic programming method previously lacked theoretical justification. This paper puts the method on a sounder footing by recasting the dynamic programming as inference on an ensemble of hidden Markov models (HMMs). Although some work has questioned the use of score outputs from classifiers like …
Distributed Image Search In Camera Sensor Networks, Tingxin Yan, Deepak Ganesan, R. Manmatha
Distributed Image Search In Camera Sensor Networks, Tingxin Yan, Deepak Ganesan, R. Manmatha
R. Manmatha
Recent advances in sensor networks permit the use of a large number of relatively inexpensive distributed computational nodes with camera sensors linked in a network and possibly linked to one or more central servers. We argue that the full potential of such a distributed system can be realized if it is designed as a distributed search engine where images from different sensors can be captured, stored, searched and queried. However, unlike traditional image search engines that are focused on resource-rich situations, the resource limitations of camera sensor networks in terms of energy, band- width, computational power, and memory capacity present …
A Discrete Direct Retrieval Model For Image And Video Retrieval, Shaolei Feng, R. Manmatha
A Discrete Direct Retrieval Model For Image And Video Retrieval, Shaolei Feng, R. Manmatha
R. Manmatha
This paper proposes a formal framework for image and video retrieval using discrete Markov random fields(MRF). The training dataset consists of images with keywords (regions are not labeled). The model may be built using quantized region or point features generated from the training images. Unlike many previous techniques, our MRF based model doesn't require an explicit annotation step for retrieval. The model directly ranks all test images according to the posterior probability of an image given a query. Image and video retrieval experiments are performed on two standard datasets (one Corel datasets and a TRECVID3 dataset) which consist of 4,500 …