An Image Retrieval System


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INTRODUCTION

With the rapid growth of internet and multimedia systems, the use of visual information has increased enormously, such that image indexing and retrieval have become important. These techniques extract meaningful information (features) from an image so that images can be classified and retrieved efficiently based on their contents.
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming and expensive. To address this, there has been a large amount of research done on automatic image annotation.

Challenges:

Bridging the semantic gap
Neither a single features nor a combination of multiple visual features could fully capture high level concept of images. Besides, due to the performance of Image retrieval based on low level features are not satisfactory, there is a need for the mainstream of the research converges to retrieval based on semantic meaning by trying to extract the cognitive concept of a human to map the low level image features to high level concept (semantic gap). In addition, representing image content with semantic terms allows users to access images through text query which is more intuitive, easier and preferred by the front end users to express their mind compare with using images. For example, users’ queries may be ‘Find an image of sunset rather than ‘find me an image contains red and yellow colors’. Semantic representation of images can be done through the process as shown in Fig 1.1.

Image Content Descriptors

Generally speaking, image content may include both visual and semantic content. Visual content can be very general or domain specific. General visual content include color, texture, shape, spatial relationship, etc. Domain specific visual content, like human faces, is application dependent and may involve domain knowledge. Semantic content is obtained either by textual annotation or by complex inference procedures based on visual content. A visual content descriptor can be either global or local. A global descriptor uses the visual features of the whole image, whereas a local descriptor uses the visual features of regions or objects to describe the image content. To obtain the local visual descriptors, an image is often divided into parts first. The simplest way of dividing an image is to use a partition, which cuts the image into tiles of equal size and shape.

COLOR

Color is the most extensively used visual content for image retrieval. Its three-dimensional values make its discrimination potentiality superior to the single dimensional gray values of images. Before selecting an appropriate color description, color space must be determined first.