Content-based image retrieval (CBIR) System
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22-04-2010, 12:18 AM


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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svjagan4u@gmail.com
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#2
25-05-2010, 04:17 PM

c# code for content based image retrieval
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Sidewinder
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26-05-2010, 10:39 AM

I don't know if its c#, but this link gives a code for content based image retrieval. :
codeproject and implimentationKB/graphics/cbir.aspx
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08-06-2010, 09:48 PM

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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prajakta13july
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#5
23-09-2010, 09:14 PM

plz send seminar and presentation report on content based image retrieval....
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15-10-2010, 11:45 AM



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
15-10-2010, 04:06 PM


.doc   CONTENED BASED IMAGE RETRIVAL MODULES.doc (Size: 74 KB / Downloads: 143)


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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mohancsevzm
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17-04-2011, 03:14 PM

i want project and implimentation of cbir
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mohancsevzm
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01-05-2011, 06:52 AM

i want ppt of content based image retrieval system ppt
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dvidyamit
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23-09-2011, 01:20 PM

I need a document for CBIR
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24-09-2011, 10:04 AM

To get more information about the topic "Content-based image retrieval (CBIR) System " please refer the link below

topicideashow-to-content-based-image-retrieval-cbir-system?pid=56830#pid56830

topicideashow-to-content-based-image-retrieval-cbir-system
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#12
07-04-2012, 01:38 PM

Content-based image retrieval


.docx   DOCUMENTATION.docx (Size: 885.53 KB / Downloads: 75)


INTRODUCTION

CONTENT BASED IMAGE RETRIEVAL

"Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.



CBIR USING COLOUR AND TEXTURE

Color is one of the most widely used visual features and is invariant to image size and orientation . As conventional color features used in CBIR, there are color histogram, color correlogram , color structure descriptor (CSD), and scalable color descriptor (SCD). The latter two are MPEG-7 color descriptors. Color histogram is the most commonly used color representation, but it does not include any spatial information. On the other hand, color correlogram describes the probability of finding color pairs at a fixed pixel distance and provides spatial information. Therefore color correlogram yields better retrieval accuracy in comparison to color histogram . Color autocorrelogram is a subset of color correlogram, which captures the spatial correlation between identical colors only.


KEY CONCEPTS

content-based image retrieval (CBIR) has become an active and fast-advancing research area in image retrieval . In a typical CBIR, features related to visual content such as shape, color, and texture are first extracted from a query image, the similarity between the set of features of the query image and that of each target image in a DB is then computed, and target images are next retrieved which are most similar to the query image. Extraction of good features which compactly represent a query image is one of the important tasks in CBIR. Shape is a visual feature that describes the contours of objects in an image,


AUTOCORRELOGRAM

The color correlogram was proposed to characterize not only the color distributions of pixels, but also the spatial correlation of pairs of colors. It describes the probability of finding a pixel of another special color at a fixed pixel distance for a given pixel of a color. If we consider all the possible combinations of color pairs, the size of the color correlogram will be very large. Therefore a simplified version of the feature is called color autocorrelogram.

BDIP

In images, edges represent the regions which involve abrupt change of intensity, and valleys represent the regions which contain local intensity minima. They are very important features in human vision and, especially, valleys are fundamental in the visual
perception of object shape . BDIP is a texture feature that effectively extracts edges and valleys.

BVLC

BVLC represents the variation of block-based local correlation coefficients according to four orientations. It is known to measure texture smoothness well. Each local correlation coefficient is defined as local covariance normalized by local variance.


. LITERATURE REVIEW

SIMILAR EVENTS
The content based image retrieval is essential in many fields, including Retail catalogs , Medical diagnosis , Crime prevention ,military . The future behavior of a particular image retrieval system can be predicted by studying how it has behaved up to a given time. Similarly, determining what other values can be considered in order to extract image much more efficiently and accurately and actions can be taken to improve the system. There is a growing need for content based image retrieval mainly in medical and othy designing applications.



EXISTING SYSTEM
Existing algorithm retrieve images only based on colour and only colour features are extracted from the image set to retrieve them.



LIMITATIONS OF EXISTING SYSTEM
• Existing one is does not handle high level features of images like texture which is one of the essential thing in many of the applications.
• The current colour extraction process like Euclidean distance is little is very simple and it is not accurate in medical fields and it provides less accuracy.




NEED FOR NEW SYSTEM
To improve the colour based retrieval system, such that it can also be useful to increase the retrieval accuracy. The new feature extraction method gives gives more efficiency and accuracy.


PROPOSED SYSTEM
The proposed system make use of combination both colour and texture feature extraction. And provide more efficiency by using autocorrelogram and Robert edge detection method instead of using Euclidean distance.


BENEFITS OF NEW SYSTEM
• This algorithm can handle high level features of images.
• It gives more accuracy than the existing one.
• It will reduce the search time.


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#13
03-09-2012, 05:23 PM

Content-Based Image Retrieval (CBIR)


.docx   1Content-Based Image.docx (Size: 10.71 KB / Downloads: 34)

Abstract

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.
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anmol chopra
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#14
12-10-2014, 06:11 AM

i need CBIR base paper and abstract for porject implementation.ppt also needed
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anmol chopra
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#15
12-10-2014, 06:25 AM

plz send pdf and presentation report on content based image retrieval...for project
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