Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor.

Abstract An organization uses a symbol as its representation in the market for ease of identification and uniqueness. Logos are used to identify and retrieve the materials, even in a complex environment for further analysis. Algorithms based on support vector machine and neural networks provide better results in retrieval of the document from small dataset. But inlarge data sets the existing models lags in their classification performance. This proposed model uses ant colony optimization (ACO) along with the local descriptor scale-invariant feature transform (SIFT), as a hybrid model for retrieving document from dataset. This hybrid model enhances the performance of the retrieval model in terms of increased efficiency, leading to an accuracy of 95.86% with a high output precision of 97.67%. [ABSTRACT FROM AUTHOR]