Color Histogram Features for Image Retrieval Systems

ABSTRACT: Histogram features have proved powerful in the classification of image and object detection . The CBIR most efficient and searches the color based images. Here in this method we use some improved preprocessing steps, preprocessing algorithms and the image classification is analyzed. In CBIR image classification has to be computationally very fast and efficient. In this project a new approach is introduced, which based on low level image histogram features. Color is a main powerful descriptor that often identifies object and extraction scene. The main advantage of this method is the very quick generation and comparison of the applied feature vectors. Histograms are simple to calculate in software and also lend themselves to economic hardware implementations. A popular tool for a real-time image processing histogram-based image retrieval methods in two color spaces were exhaustively compared. The testing also highlights the weaknesses and strengths of the model. KEY WORDS: color-based and content based image retrieval, K-Nearest neighbor method, histogram intersection and its similarity

Facilitating Image and Document Retrieval Using Image Content and Querying Keyword

Evolution of ubiquitous computing in the areas of personal computing technology has produced staggeringly large data It is difficult to search mainly the image data by understanding   users objective only by keywords and phrases and this leads to uncertain outcomes. For producing these outcomes effectively, this paper introduces a new approach to the problem of image learning to enable search engines to learn about visual content over time based on user feedback through one click activity and images from a pool recovered by text based query are re-ranked depending on both visual and text based query. Content Based Image Retrieval (CBIR) techniques are used for accessing semantically-relevant images from an image data source depending on automatically-derived image functions for features like Geometric moments, Global histogram, Color Moments, Local histogram. Documents can also be retrieved using the text based query by the user

Content Based Image Retrieval by Preprocessing Image Database

Increase in communication bandwidth, information content and the size of the multimedia databases have given rise to the concept of Content Based Image Retrieval (CBIR). Content based image retrieval is a technique that enables a user to extract similar images based on a query, from a database containing a large amount of images. A basic issue in designing a content based image retrieval system is to select the image features that best represent image content in a database. Current research in this area focuses on improving image retrieval accuracy. In this work, we have presented an ecient system for content based image retrieval. The system exploits the multiple features such as color, edge density, boolean edge density and histogram information features. The existing methods are concentrating on the relevance feedback techniques to improve the count of similar images related to a query from the raw image database. In this thesis, we propose a dierent strategy called preprocessing image database using k means clustering and genetic algorithm so that it will further helps to improve image retrieval accuracy. This can be achieved by taking multiple feature set, clustering algorithm and tness function for the genetic algorithms. Preprocessing image database is to cluster the similar images as homogeneous as possible and separate the dissimilar images as heterogeneous as possible. The main aim of this work is to nd the images that are most similar to the query image and new method is proposed for preprocessing image database via genetic algorithm for improved content based image retrieval system. The accuracy of our approach is presented by using performance metrics called confusion matrix, precison graph and F-measures. The clustering purity in more than half of the clusters has been above 90 percent purity