A Parallel Algorithm For Image Semantic Information Searching

The amount of multimedia information is rapidly increasing duo to digital cameras. To interpret semantic of image, many researcher use keywords as textual annotation. Image semantic information retrieval became recently attractive for many peoples. Concept recognition is a key problem in semantic information searching. A new semantic concept framework GIO was built. Based on GIO, we proposed a parallel algorithm for semantic concept mapping, which adopts two-stages concept searching method. The first stage is to implement image low-level feature extraction schema; the second step is to implement latent semantic concept model searching, and bridging relationship between image low-level feature and global sharable ontology. Through combining ontology and image low-level feature, the images on web pages and semantic concept can be mapping together for semantic searching. Experiments on several web pages sets show that it can outperform other methods in terms of precision and recall.