Since the last decade, the complexity of multimedia data, specifically images, is emerging exponentially as millions of images are uploaded by users on daily basis.Searching for Label Console Window a relevant image from such a substantial amount of data is very hectic and resource-demanding.To cope with this issue, researchers are working on content-based image retrieval (CBIR) approaches.This article proposes an efficient and novel probabilistic technique as a solution for content-based image retrieval.
The patterns formed by the glyph structure of an image are excavated to yield content representations.These representations are accumulatively used to form a distribution, whereas the characteristics of this distribution represent the semantic structure of the image.In the end, the mixture model for gamma distribution is applied and parameters are refined through maximum likelihood.Furthermore, a mechanism is devised to retrieve matching images having comparable distribution patterns.
Experiments show not only that the proposed technique yields a comparable precision to Vitamins D other competitive techniques but it also demonstrates that it is sufficiently efficient with high performance compared as compared to the others and requires unsupervised training.