Sagarmay Deb

An overview of content-based image retrieval techniques

Content-based image retrieval is currently a very important area of research in the area of multimedia databases. Plenty of research work has been undertaken to design efficient image retrieval techniques from image or multimedia databases. Although a large number of indexing and retrieval techniques have been developed, there are still no universally accepted feature extraction, indexing and retrieval techniques available. In this paper, we present an up-to-date review of various content-based image retrieval systems. Since the volume of literature available in the field is enormous, only selected works are mentioned.

Multimedia Systems and Content-Based Image Retrieval

Multi-media systems and content-based image retrieval are areas that are changing our lifestyles because they cover creation, maintenance, accessing and retrieval of video, audio, image, textual and graphic data. Yet some issues in these areas remain unresolved and this work highlights such issues.

Video Data Management and Information Retrieval

In this chapter, we present a basic introduction to two very important areas of research in the domain of Information Technology, namely, video data management and video information retrieval. Both of these areas need additional research efforts to seek solutions to many unresolved problems for efficient data management and information retrieval. We discuss those issues and relevant works done so far in these two fields. INTRODUCTION An enormous amount of video data is being generated these days all over the world. This requires efficient and effective mechanisms to store, access, and retrieve these data. But the technology developed to date to handle those issues is far from the level of maturity required. Video data, as we know, would contain image, audio, graphical and textual data. The first problem is the efficient organization of raw video data available from various sources. There has to be proper consistency in data in the sense that data are to be stored in a standard format for access and retrieval. Then comes the issue of compressing the data to reduce the storage space required, since the data could be really voluminous. Also, various features of video data have to be extracted from low-level features like shape, color, texture, and spatial relations and stored efficiently for access. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.irm-press.com IRM PRESS This chapter appears in the book, Video Data Management and Information Retrieval by Sagarmay Deb. Copyright

Human Perception based Image Retrieval using Emergence Index and Fuzzy Similarity Measure

The main concern dealing with content-based image retrieval (CBIR) is to bridge the semantic gap. The high level query posed by the user and low level features extracted by the machine illustrates the problem of semantic gap. To solve the problem of semantic gap, this paper presents a hybrid technique using an emergence index and fuzzy logic for efficient retrieval of images based on the colour feature. Emergence index (EI) is proposed to understand the hidden meaning of the image. Fuzzy similarity measure is developed to calculate the similarity between the target image and the images in the database. The images were ranked based on their similarity along with the fuzzy similarity distance measure. The preliminary experiments conducted on small set of images and promising results were obtained.

Image Retrieval in Multimedia with Emergence Index

Emergence is a phenomenon where we study the implicit or hidden meaning of an image. We introduce this concept in image database access and retrieval of images using this as an index for retrieval. This would give an entirely different search outcome than ordinary search where emergence is not considered, as consideration of hidden meanings could change the index of search. We discuss emergence, emergence index and accessing multimedia databases using emergence index in this paper.