13-01-2011, 01:41 PM
[attachment=8187]
Submitted to:
Ms. Mamta Sakpal
Ms. Sushma Khatri
Ms. Preeti Jain
Submitted by:
Prakhar Jain (67)
Pranjal Solanki(69)
Prerna Sisodia(72)
Mr. Raman Bhati
1. Introduction
1.1 Purpose
In last few years the potential growth in digitization of images has occurred,with immense amount of information flowing and stored in the database of world wide web.the No. of users exploiting the WWW has increased tremendously while accessing and manipulate remotely-stored images in all kinds of new and exciting ways.However, they are also discovering that the process of locating a desired image in a large and varied collection can be a source of considerable frustration.The problems of image retrieval are becoming widely recognized, and the search for solutions an increasingly active area for research and development.Some indication in form of No. of research and development in field of CBIR.No.of journal articles,research papers, appearing each year on this subject.
Traditional way to search image in database is to create a textual description of all the images in the database and use the methods from text-based information retrieval to search based on the textual descriptions.Unfortunately, this method is not feasible. On the one hand
annotating images has to be done manually and is a very time-consuming task and on the
other hand images may have contents that words cannot convey.
This has given rise in interest of techniques for retrieving images on the basis of automatically-derived features such as colour, texture and shape – a technology now generally referred to as Content-Based Image Retrieval (CBIR)
1.2 Scope
The software product is content based image reteival (CBIR) is about developing an image search engine, not only by using the text annotated to the image by an end user (as traditional image search engines), but also using the visual contents available into the images itselves.
Initially, CBIR system should has a database, containing several images to be searched. Then, it should derive the feature vectors of these images, and stores them into a data structure like on of the “Tree Data Structures” (these structures will improve searching efficiency).
A CBIR system gets a query from user, whether an image or the specification of the desired image. Then, it searchs the whole database in order to find the most similar images to the input or desired image.
CBIR usually deals with large image collection of low level and high level features,which directly influence indexing and retival complexity,memorey and disk space requirment.due to high memorey and processing power requirment,cbir has not widely been appplied on platforms having limited resouces,such as mobile devices
1.3 Definitions, Acronyms, and Abbreviations.
CBIR: content based image retrieval
QBIC: query by image content
CBVIR :content-based visual information retrieval
definitions:
● A feature vector ~vˆI of an image can be thought of as a point in Rn space: ~vˆI =
(v1, v2, ..., vn), where n is the dimension of the vector.
Examples of possible feature vectors are a color histogram [14], a multiscale fractal
curve [15], and a set of Fourier coefficients [16]
● The row mean vector is the set of averages of the intensity
values of the respective rows.
● The column mean vector is the set of averages of the intensity values of the respective
