Microsoft Technology Licensing, LLC

Liu Wenyin

Zheng Chen

Hong-Jiang Zhang

Mingjing Li

Xiaoming Sun

The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.

Statistical bigram correlation model for image retrieval

The disclosed subject matter models or predicts a user's intention during network or WWW navigation. Specifically, a statistical multi-step n-gram probability model is used to predict a user's optimal information goal. The optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

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Beijing, China

Liu Wenyin