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

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 in- large 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 hy- brid model enhances the performance of the retrieval model in terms of increased effi- ciency, leading to an accuracy of 95.86% with a high output precision of 97.67%.