Semantic extraction of local image features based on expectation maximization (E M) algorithm. First extract the local features of the image, the visual vocabulary in the frequency of statistical feature, the image into the bag of words model introduce the latent semantic analysis of the text the technology to establish the mapping model between image low-level features to high-level semantic image and then use the E M algorithm for fitting probability model, probabilistic latent semantic distribution of local image features the distribution of the final image by using the model extracted in the latent semantic of image analysis and understanding.

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