Region based multiple features Integration and multiple-stage feedback latent semantic image retrieval method
A multi-feature fusion and image retrieval technology, which is applied in the image retrieval of integrated text and image content, and latent semantic image retrieval, can solve the problems of not considering multi-feature fusion retrieval of images, low matching accuracy, single feature selection, etc., to achieve Improve the retrieval accuracy, increase the accuracy rate, and overcome the effects of versatility
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[0039] The image database adopted by the implementation example of the present invention is 3 million images collected from the Internet, including heterogeneous images of various semantic categories, including: natural scenery, characters, animals, plants, urban buildings, vehicles, daily necessities, etc. . The feature extraction of each image is processed offline in the background. The extraction of the underlying visual features is: first use the watershed algorithm to segment the image, and then use the fuzzy C-means to achieve regional fusion to form 6 (6 are more in line with human visual characteristics. ) region (or object), and then for each region extract its L * u * The color average value (3 dimensions), co-occurrence texture (9 dimensions) and area area ratio (1 dimension) of the V space are combined into a 78-dimensional (78=13×6) comprehensive visual feature. The eigenvector is represented by a vector, T={x ij |i=1, 2, ..., M; j = 1, 2, ..., 78, where M is t...
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