02-07-2013, 04:55 PM
Image Retrieval Techniques based on Image Features: A State of Art approach for CBIR
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Abstract
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this paper outlines a description of the primitive feature extraction techniques like: texture, colour, and shape. Once these features are extracted and used as the basis for a similarity check between images, the various matching techniques are discussed. Furthermore, the results of its performance are illustrated by a detailed example.
INTRODUCTION
As processors become increasingly powerful, and memories become increasingly cheaper, the deployment of large image databases for a variety of applications have now become realisable. Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields — for example, geography, medicine, architecture, advertising, design, fashion, and publishing. Effectively and efficiently accessing desired images from large and varied image databases is now a necessity
CBIR or Content Based Image Retrieval is the retrieval of images based on visual features such as colour, texture and shape. Reasons for its development are that in many large image databases, traditional methods of image indexing have proven to be insufficient, laborious, and extremely time consuming. These old methods of image indexing, ranging from storing an image in the database and associating it with a keyword or number, to associating it with a categorized description, have become obsolete.
Problem Statement
The problem involves entering an image as a query into a software application that is designed to employ CBIR techniques in extracting visual properties, and matching them. This is done to retrieve images in the database that are visually similar to the query image.
Methods of Representation
The main method of representing colour information of images in CBIR systems is through colour histograms. A colour histogram is a type of bar graph, where each bar represents a particular colour of the colour space being used. In MatLab for example you can get a colour histogram of an image in the RGB or HSV colour space. The bars in a colour histogram are referred to as bins and they represent the x-axis. The number of bins depends on the number of colours there are in an image. The y-axis denotes the number of pixels there are in each bin. In other words how many pixels in an image are of a particular colour.
TEXTURE
Texture is an intuitive concept that describes
properties like smoothness, coarseness, and regularity of
a region. Texture is an important element to human
vision, it provides cues to scene depth and surface
orientation. In the next sections, Intensity-based texture
will be described which has been the topic of
investigation for many years and has proven useful. For
example, the black and white television proves the
usability of Intensity-based texture: people are able to
see 3D in a 2D black and white screen. So, it seems
important to look at Intensity-based textures before
looking at colourful textures because the techniques
used by Intensity-based textures can probably be
expanded to colour-texture.
Texture Definition
Texture is that innate property of all surfaces
that describes visual patterns, each having properties of
homogeneity. It contains important information about
the structural arrangement of the surface, such as; clouds,
leaves, bricks, fabric, etc. It also describes the
relationship of the surface to the surrounding
environment. In short, it is a feature that describes the
distinctive physical composition of a surface.
Texture may be defined as a local arrangement of image
irradiances projected from a surface patch of
perceptually homogeneous irradiances. Texture regions
give different interpretations at different distances and at
different degrees of visual attention. At a standard
distance with normal attention, it gives the notion of
macro-regularity that is characteristic of the particular
texture. When viewed closely and attentively,
homogeneous regions and edges, sometimes constituting
texels, are noticeable.
IMAGE FEATURE MATCHING
Similarity Distance measures
CBIR employs low level image features such as color, shape or texture to achieve objective and automatic indexing, in contrast to subjective and manual indexing in traditional image indexing. For contend based image retrieval, the image feature extracted is usually an N-dimensional feature vector which can be regarded as a point in a N-dimensional space. Once images are indexed into the database using the extracted feature vectors, the retrieval of images is essentially the determination of similarity between the features of query image and the features of target images in database, which is essentially the determination of distance between the feature vectors representing the images. The desirable distance measure should reflect human perception.