Content Based Image Retrieval Case Study

A. Blaser had presented Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users' interests, has been an active and fast advancing research area since the 1990s. During the past decade, remarkable progress has been made in both theoretical research and system development. However, there remain many challenging research problems that continue to attract researchers from multiple disciplines.
Before introducing the fundamental theory of content-based retrieval, we will take a brief look at its development. Early work on image retrieval can be traced back to the late 1970s. In 1979, a conference on Database Techniques for Pictorial Applications was held in Florence.
Different CBIR systems have adopted different techniques. A color histogram describes the global color distribution in an image. While the color histogram is robust to translation of object and rotation about the viewing axis, it does not include any spatial information, Different images can have same color distributions and large appearance changes in an image can easily change the histogram. Thus the use of object-specific information contained in images is essential for efficient image
Traditionally, content-based multimedia retrieval was supported by augmenting multimedia objects with textual annotations. Information retrieval techniques on textual descriptions were then used to support content-based retrieval. There are numerous limitations of this approach. First, the approach is not scalable since each object needs to be manually annotated with keywords and/or textual descriptions making it impractical for large data sets. Second, due to the subjectivity of the human annotator, the annotations may not be consistent which negatively affects retrieval effectiveness. Furthermore, it may be infeasible to describe visual content (e.g., shape of an object) using simply words. To overcome the above problems, over the past few years content-based retrieval over visual features has emerged as a promising research direction. This is evidenced by several prototype and commercial systems. In the visual feature based approaches, image processing techniques are used to extract visual features from images. Examples of visual features are: color, texture and shape for images, and motion parameters for video. Multimedia objects are represented as a collection of visual features extracted instead of just pure textual annotations (object model). A user formulates a query by