Semantic Image Retrieval Using Multiple Features

In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar images, the need for databases, the semantic gap, and retrieving the desired images from huge collections are the keys to improve. CBIR system analyzes the image content for indexing, management, extraction and retrieval via low-level features such as color, texture and shape. To achieve higher semantic performance, recent system seeks to combine the low-level features of images with high-level features that conation perceptual information for human beings. Performance improvements of indexing and retrieval play an important role for providing advanced CBIR services. To overcome these above problems, a new query-by-image technique using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.