Content-based image retrieval (CBIR) System
#1

Content-based image retrieval

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases.

"Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, searches must rely on metadata such as captions or keywords, which may be laborious or expensive to produce.

CBIR software systems and techniques

Query techniques
Different implementations of CBIR make use of different types of user queries.

Query by example
Query by example is a query technique that involves providing the CBIR system with an example image that it will then base its search upon. The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example.

Options for providing example images to the system include:

A preexisting image may be supplied by the user or chosen from a random set.
The user draws a rough approximation of the image they are looking for, for example with blobs of color or general shapes.
This query technique removes the difficulties that can arise when trying to describe images with words.

Other query methods
Other methods include specifying the proportions of colors desired (e.g. "80% red, 20% blue") and searching for images that contain an object given in a query image.

CBIR systems can also make use of relevance feedback, where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information.

Content comparison techniques
The sections below describe common methods for extracting content from images so that they can be easily compared. The methods outlined are not specific to any particular application domain.

Color
Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color searches will usually involve comparing color histograms, though this is not the only technique in practice.

Texture
Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located.

Shape
Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. In some cases accurate shape detection will require human intervention because methods like segmentation are very difficult to completely automate.

The CBIR system is developed using ASP.NET with C#. It can be developed in other programming languages like J2EE.
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#2
c# code for content based image retrieval
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#3
I don't know if its c#, but this page link gives a code for content based image retrieval. :
http://codeprojectKB/graphics/cbir.aspx
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#4
Actually the Content “ based image retrieval (CBIR) is a technique used for extracting similar images from an image database. This technique uses visual contents to search for extracting similar images from an image database. This technique uses visual contents to search images from large scale image databases according to user™s interests.
It uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. In typical content-based image retrieval systems the visual contents of the images in the database are extracted and described by multi-dimensional feature vectors. The feature vectors of the images in the database form a feature database. To retrieve images, users provide the retrieval system with example images or sketched figures. The system then changes these examples into its internal representation of feature vectors. The similarities /distances between the feature vectors of the query example or sketch and those of the images in the database are then calculated and retrieval is performed with the aid of an indexing scheme. The indexing scheme provides an efficient way to search for the image database. Recent retrieval systems have incorporated users' relevance feedback to modify the retrieval process in order to generate perceptually and semantically more meaningful retrieval results
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#5
plz send seminar report on content based image retrieval....
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#6


Content Based Image Retrieval (CBIR)

ABSTRACT


Content Based Image Retrieval CBIR is becoming very popular because of the high demand for searching image databases of ever-growing size. Since speed and precision are important, we need to develop a system for retrieving images that is both efficient and effective.


The emergence of multimedia technology and the rapidly expanding image and video collections on the internet have attracted significant research efforts in providing tools for effective retrieval and management of visual data. Content Based Image retrieval CBIR is based on the availability of a representation scheme of image content. Thus CBIR can be used as a powerful tool for retrieving images from the database by utilizing the visual cues alone. Image descriptors may be visual features such as color, texture and shape.



The motivation of our approach is to design and implement an effective and efficient framework of image retrieval techniques, using a variety of visual features such as color and texture. When a query image is given to our system, these features are extracted from it and compared with those in the database based on similarity measure. Finally twenty images which are most relevant to the query image are retrieved. We implemented Histogram Quadratic Distance Measure as color similarity which is most efficient Thus we have implemented both color and texture giving efficiency to our system.





INTRODUCTION

Content-based image retrieval is one of the techniques for automatic retrieval of images from a database by color, texture etc. The features used for retrieval can be either primitive or semantic but the abstraction process must be predominantly automatic.
The goal of Content-Based Image Retrieval (CBIR) systems is to operate on collections of images and, in response to visual queries, extract relevant image. The application potential of CBIR for fast and effective image retrieval is enormous, expanding the use of computer technology to a management tool.

Existing Systems
IBM’s QBIC system is the first commercial CBIR system and probably the best known of all CBIR systems. QBIC supports users to retrieval images by color, shape and texture. QBIC provides several query methods: Simple, Multi-feature and Multi-pass. In the simple method, a query is processed using only one feature.


OUR APPROACH

Query image is taken as the input for processing and is normalized to 320*480 sizes. When our system receives the query message it passes the image and weights of the features to the feature extraction mechanism. After the features are extracted, the feature extraction mechanism sends the feature information of the query image to the similarity measure mechanism. According to the feature information, the similarity measure mechanism measures the similarity of the features information between the query image and the database images. Finally, our system gives the most relevant images as the output along with the query image.




Hardware Requirements:
Pentium 4 Processor
1 GB RAM
80 GB Hard Disk Space

Software Requirements:
Microsoft Windows Xp Professional.
Sun JDK 1.6
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#7
[attachment=6204]


INTRODUCTION
PURPOSE OF THESIS

The need for Content- Based image retrieval is to retrieve images that are more appropriate, along with multiple features for better retrieval accuracy. Usually in search process using any search engine, which is through text retrieval, which won’t be so accurate. So, we go for Content- Based image retrieval. Content- Based Image Retrieval also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR). “Content-based” means that the search makes use of the contents of image themselves, rather than relying on human-inputted metadata such as captions or keywords. The similarity measurements and the representation of the visual features are two important issues in Content-Based Image Retrieval (CBIR).
Given a query image, with single / multiple object present in it; mission of this work is to retrieve similar kind of images from the database based on the features extracted from the query image. In this we use features like color, texture and shape features.

OBJECTIVE OF THESIS

The main objective of this thesis work is to retrieve images that are similar to query image from a large database. We use content- based search, for high accuracy multiple features like color, texture and shape is incorporated. Color feature extraction is done through “Global Color Histogram (GCH)” and “Local Color Histogram”, Shape through “Geometric Moments” and Texture through “Co- Occurrence” & “Edge Frequency”.
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#8
i want project of cbir
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#9
i want ppt of content based image retrieval system ppt
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#10
I need a document for CBIR
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#11
To get more information about the topic "Content-based image retrieval (CBIR) System " please refer the page link below

http://studentbank.in/report-content-bas...0#pid56830

http://studentbank.in/report-content-bas...bir-system
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#12
Content Based Image Retrieval


.ppt   Content Based Image Retrieval.ppt (Size: 1.41 MB / Downloads: 34)

ABSTRCT

Images play an important role in conveying information. With the rapid development of computer technology, the amount of digital imagery data is rapidly increasing. There is an inevitable need for efficient methods that can help in searching for and retrieving the visual information that a user is interested in. The manual annotation of images is becoming more and more an infeasible process. An ever flourishing retrieval technique is content based image retrieval (CBIR), where the visual contents found in the images are exploited for representing and retrieving the images.

INTRODUCTION

Image retrieval system
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images.
Types of image retrieval
Text based image retrieval.
Content based image retrieval
Limitations of text based image retrieval
Annotating large volume of databases is time consuming and expensive.
Subjectivity of human perception and too much responsibility on the end-user
Problem of deeper (abstract) needs like queries that cannot be described at all, but tap into the visual features of images.

LIMITATIONS OF CBIR SYSTEM

Bridging The Semantic Gap
The semantic representation of an image can be done as follows:
The image extraction process will get the low level features of images either by color, shape, textures and spatial.
These low level features can be clustered or segmented based on the similar characteristics of the visual features to form some regions representation and next to form objects representation in the images.
The regions/objects representation will be annotated with keyword by image annotation process. This annotation process can be done either manually, semi automatically or automatically.
The image then will be represented using semantics and image retrieval can be queried based on high level concept.

CONCLUSION

CBIR aims at avoiding the use of textual descriptions and instead retrieves images based on similarities in their contents (textures, colors, shapes etc.) to a user-supplied query image or user-specified image features. General visual features most widely used in content-based image retrieval are color, texture, shape, and spatial information. Color is usually represented by the color histogram, and color moment under a certain color space. Texture can be represented by Tamura feature, Gabor and Wavelet transformation. Shape can be represented by turning angles, Fourier descriptors. CBIR scheme in the DCT domain is suitable for retrieval of color JPEG images of different sizes.




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#13
Plz provide matlab code for content based image retrieval. My email id : den.biswa[at]gmail.com
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#14
To get full information or details of Content-based image retrieval (CBIR) System please have a look on the pages

http://studentbank.in/report-content-bas...e=threaded

http://studentbank.in/report-cbir-conten...e=threaded

http://studentbank.in/report-content-bas...e=threaded

http://studentbank.in/report-content-bas...bir-system

http://studentbank.in/report-content-bas...e=threaded

if you again feel trouble on Content-based image retrieval (CBIR) System please reply in that page and ask specific fields in Content-based image retrieval (CBIR) System
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#15
i could not open any document related to the topic content based image retrieval system
i need functional requirements of the content based image retrieval system
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#16
To get full information or details of Content-based image retrieval (CBIR) System please have a look on the pages

http://studentbank.in/report-content-bas...#pid177306

if you again feel trouble on Content-based image retrieval (CBIR) System please reply in that page and ask specific fields in Content-based image retrieval (CBIR) System
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