For a hybrid medical image retrieval system, a genetic algorithm (GA) approach is presented for the selection of dimensionality reduced set of features. This system was developed in three phases. In first phase, three distinct algorithm are used to extract the vital features from the images. The algorithm devised for the extraction of the features are Texton based contour gradient extraction algorithm, Intrinsic pattern extraction algorithm and modified shift invariant feature transformation algorithm. In the second phase to identify the potential feature vector GA based feature selection is done, using a hybrid approach of “Branch and Bound Algorithm” and “Artificial Bee Colony Algorithm” using the breast cancer, Brain tumour and thyroid images. The Chi Square distance measurement is used to assess the similarity between query images and database images. A fitness function with respect Minimum description length principle were used as initial requirement for genetic algorithm. In the third phase to improve the performance of the hybrid content based medical image retrieval system diverse density based relevance feedback method is used. The term hybrid is used as this system can be used to retrieve any kind of medical image such as breast cancer, brain tumour, lung cancer, thyroid cancer and so on. This machine learning based feature selection method is used to reduce the existing system dimensionality problem. The experimental result shows that the GA driven image retrieval system selects optimal subset of feature to identify the right set of images

Lack of today's technology of information retrieval system is the semantic between the information resources. The semantic can be expressed for a resource using the underlying technology of ontology. So the main objective of this paper is to design an information retrieval system for both text and image data using the concept of ontology. The main focus of this paper is to improve information retrieval for sports events using Ontologies. As it is complex problem, it is addressed into the following sub-problems in this paper. (1) Integrating domain knowledge and images using fuzzy ontology and Retrieving the required Muti-modal information using fuzzy rule set. (2) Providing image semantic by constructing visual codebook for affine covariant-Semantic segmented patches. (3) Analysing the discriminative power of each visual word using the probabilistic latent semantic and quantizing them using the Chi-Square test. In this work, the domain of Sports is considered for creating both Low-level visual ontology for certain sport event images and also for building a high-level domain ontology from the information on web. These two Ontologies are integrated using Fuzzy concepts