Content Based Image Re-ranking using Indexing Methods

With increase in number of digital images, retrieval of images efficiently becomes an important topic for research. Traditional methods for image retrieval used metadata associated with images, commonly known as keywords. These methods empowered many World Wide Web search engines and achieved reasonable amount of accuracy. A novel image re-ranking framework propose, which offline learns different visual semantic spaces automatically for different entered query keywords by using keyword expansions. We have projected the visual features of images into their associated visual semantic spaces, to generate the semantic signature. Images are re-ranked by comparing the semantic signature of images in online stage. CBIR uses contents of image, such as shape, colour, texture or any other information that can be derived from image itself. Although there are many problems associated with CBIR method. Amongst them semantic gap with image features has received a lot of attention. Images are represented by low-level features and it is important to reduce semantic gap between high-level and low-level features of images to retrieve visual similar images and re-ranking of images. This study proposes latent semantic indexing (LSI) method to re-rank images that are retrieved using image retrieval method. A modified approach of LSI; PLSI evaluates the association degree of each document to each subject, and then categorize the search results into subjects by using that information. In this project we propose a Markovian Semantic Indexing (MSI) is offered in the framework of an online image retrieving system. For Annotation-Based Image Retrieval (ABIR) tasks, the properties of MSI make it particularly suitable when the per image annotation data is limited. In the context of online image retrieval systems the characteristics of this method is mostly relevant. Keywords—Image search, semantic space, semantic signature, image re-ranking, keyword expansion, LSI, PLSI,