Bringing High Level Meanings from an Image

Content-based image retrieval is a difficult area of research in multimedia systems. The research has proved extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. To segment the image to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The gap between low-level features like color, shape, texture, spatial relationships and high-level definitions of the images is called the semantic gap. It is very important we find a viable solution of how to extract meaningful definition of an image from low level features so that we can identify the image automatically without any human intervention. As we know billions of images are being generated from various sources all over and it’s extremely time consuming and expensive to identify these images manually. When we can identify these vast number of images automatically without human intervention then we can classify them and create databases based on these classifications. These databases could then be used to enhance machine learning of artificial intelligence. Until we solve these problems in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this paper we explore the possibilities of how we can extract high-level meanings from an image before or after the segmentation of the image in an automatized way. We attempt to use a database of data dictionary for the purpose which we believe has the potential for solving the problem of semantic gap in content-based image retrieval.