Cluster Based Image Retrieval

ABSTRACT: Typical content-based image retrieval (CBIR) system query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme CLUster-based rEtrieval of images by unsupervised learning which tackles the semantic gap problem based on a hypothesis: semantically images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query therefore; clusters give the algorithm as well as the user's semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure. Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60,000 images from COREL demonstrate improved performance

Comparative Analysis of advanced Face Recognition Techniques

ABSTRACT: This project entitled "Comparative analysis of advanced Face Recognition Techniques", it is based on fuzzy c means clustering and associated sub neural network. It deals with the face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, it represents a method for face recognition base on similar neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing effectiveness decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combine to obtain the recognition result. The facial feature vector was compared by PCA and LDA methods. In particular, the proposed method achieved 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the LDA based face recognition system

Mining User Profile Using Clustering From Search Engine Logs

ABSTRACT: Fundamental component of any personalization application is user profiling. The existing user profiling strategies are based on users interest (i.e. positive preferences).The main focus is on search engine personalization and to develop several concept-based user profiling methods. Concept-based user profiling methods deals with both positive and negative preferences. This user profiles can be integrated into the ranking algorithm of a search engine so that search result can be ranked according to individual users interest. The RSCF makes a search of data containing the item in the search results, the required data is been clicked by the user and this clicked data is given as the input and generates the rankers as the output.. The negative preference increases the separation between the similar and dissimilar queries. This separation provides a clear threshold for agglomerative clustering algorithm and improves the overall quality

KNOWLEDGE BASED WEB SERVICE

ABSTRACT: Web Services are self described software entities which can be advertised, located and used across the internet using a set of standards such as SOAP, WSDL and UDDI. In order for web services to be able to work well together, they must participate in a set of shared organizing principles known as Service Oriented Architecture (SOA).Service oriented means that the architecture is described and organized to support web service's dynamic, automated description, publication, discovery and use. The number of services published over the internet is growing at an explosive speed. So it is difficult for service requesters to select satisfactory web services, which provide similar functionality. The Quality of service is considered the most important criterion for service filtering. In this paper, the web service description models consider the service QOS information and present an overall web service selection and ranking for fulfilling service requester's functional and non functional requirements. The service selection method is based on particle swarm optimization technique. By using this multi objective Particle swarm optimization technique, a number of QOS values can be optimized at the same time and it ultimately improve the service performance. This method can significantly improve the problem solving speed and reduce the selection complexity