15-10-2010, 04:06 PM
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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”.
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”.
