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Digital images are composed of pixels. Each pixel represents the colour at a single point of the image. Rectangular array of pixels are called as a bitmap or a digital image. Advance development of image procurement and storage technology have lead to marvellous development in very huge and detailed image databases . A massive volume of image data such as digital photographs, medical images and satellite images are generated every day .
Image mining can automatically extract meaningful information from a huge of image data are increasingly in demand. It is an interdisciplinary venture that essentially draws upon expertise in artificial intelligence, computer vision, content based image retrieval, database, data mining, digital image processing and machine learning. Image mining frameworks  are grouped into two broad categories: function-driven and information-driven. The problem of image mining combines the areas of content-based image retrieval, data mining, image understanding and databases. Image mining techniques include image retrieval, image classification, image clustering, image segmentation, object recognition and association rule mining.
Image Retrieval is performed by matching the features of a query image with those in the image database. The collection of images in the web are growing larger and becoming more diverse. Retrieving images from such large collections is a challenging problem. The research communities study about image retrieval from different angles, one being text-based and the other visual-based. The text-based Image retrieval applies traditional text retrieval techniques to image annotations. The content-based Image retrieval apply image processing techniques to first extract image features and then retrieve relevant images based on the match of these features.
The rest of this paper is organized as follows. Section 2 discusses about the related work of image retrieval. Section 3 and 4 gives a text and content based image retrieval. Section 5 discussed the conclusion.
II. RELATED WORK
Digital images are currently widely used in medicine, fashion, architecture, face recognition, finger print recognition and bio-metrics etc. Recently, Digital image collections are rapidly increased very huge level. That image contains a huge amount of information. Conversely, we cannot make it information as useful unless it is organized so as to allow efficient browsing, searching, and retrieval.
Image retrieval has been a very dynamic research area. two major research communities such as database management and computer vision has study image retrieval from different angles, one being text-based and the other content-based. Late 1970s, the text-based image retrieval can be traced back.
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Many content-based image retrieval systems have been recently proposed: Chabot, MARS , Netra , Photobook , QBIC , Surfimage, SWIM , Virage , Visualseek  and WebSeek. These systems follow the paradigm of representing images using a set of attributes, such as color, texture and shape, which are archived along with the images.
III. Text-Based Image Retrieval
Text-based image retrieval [4, 5] can be based on annotations that were manually added for disclosing the images (keywords, descriptions), or on collateral text that is ‘accidentally’ available with an image (captions, subtitles, nearby text). It applies traditional text retrieval techniques to image annotations or descriptions. Most of the image retrieval systems are text-based, but images frequently have little or no accompanying textual information.
Keywords are words or phrases that are described content. They can be used as metadata to describe images, text documents, database records, and Web pages. Assigning keywords to images is of great interest as it allows one to retrieve, index, organize and understand large collections of image data. Keywords are used on the Web in two different ways: i) Keywords as a search terms for search engines ii) Keywords that identify the content of the website. An annotation is metadata attached to text, image, or other data. It refers to a specific part of the original data or image. Keyword annotation is the traditional text based image retrieval paradigm. In this approach, the images are first annotated manually by keywords. They can then be retrieved by their corresponding annotations. As the size of image repositories increases, the keyword annotation approach becomes infeasible. Text-based Image Retrieval has some limitations.
Limitations of Text-based Image Retrieval
• One major problem is that the task of describing image content is highly subjective.
• However, accompanying the relevant search results, there could be a large number of irrelevant search results, i.e. the precision of the text-based search can be low.
• In many situations, a few words cannot precisely describe the image content, and many words have multiple meanings.
• The perspective of textual descriptions given by an annotator could be different from the perspective of a user. A picture can mean different things to different people. It can also mean different things to the same person at different time.
• In other words, there could be a variety of inconsistencies between user textual queries and image annotations or descriptions.
IV. Content-Based Image Retrieval
Problems with text-based retrieval, we will use the content-based image retrieval (CBIR) [20, 21, 22] is the application of computer vision for retrieving the images which means searching the digital images in massive databases is very difficult. The term “Content based” means that it will search the actual content of an image. Information retrieval means the process of converting a request for information into a meaningful set of reference. CBIR is a technology that in principle helps organize digital image archives according to their visual content. This system distinguishes the different regions present in an image based on their similarity in color, texture, shape, etc. and decides the similarity between two images by reckoning the closeness of these different regions.
Content Based Image Retrieval [20, 21, 22] systems can be classified into two ways according to the type of queries: text based query and pictorial based query. In text query based query, images are defined by text information such as keywords and captions. Text features are powerful as a query, if appropriate text descriptions are given for images in an image database. However, giving appropriate descriptions must be done manually in general and it is time consuming. There are many ways one can pose a visual query. A good query method will be natural to the user as well as capturing enough information from the user to extract meaningful results. In pictorial query based systems, an example of the desired image is used as a query. To retrieve similar images with the example, image features such as colors and textures, most of which can be extracted automatically when it is used.
The CBIR system  provides two major responsibilities. One is feature extraction [11,21] is developed accurately to define the content of every image in the database. It is much smaller in size than the original image, typically of the order of hundreds of elements. The second one is similarity measurement, where a distance between the query image and each image in the database using their signatures is computed so that the top “closest” images can be retrieved.
i. Feature Extraction
Feature extraction [11, 21] is the beginning of content based image retrieval. It is the process of extracting image features to a distinguishable extent. It is a group of features called image signature. It is carried out by using colors, textures or shapes. Once obtained, visual features act as inputs to subsequent image analysis tasks such as similarity estimation, concept detection, or annotation. A feature is referred to capture a certain visual property of an image, which covers wholly for the entire image. There are various kinds of primitive features to represent an image such as color, texture and shape relationship. Since one type of features can only represent part of the image properties, a lot of work done on the combination of these features. However, there is no single “best” feature that gives accurate results in any general setting. Usually, a combination of features is minimally needed to provide adequate retrieval results.
The first and most straightforward feature for indexing and retrieving images is color. Color  is an immediately perceivable visual feature when looking at an image. Color is one of the most widely used features for image similarity retrieval. Color space is used to represent color images. However, RGB space denotes the gray level intensity is represented as the sum of red, green and blue gray level intensities Color moments have been successfully used in many retrieval systems especially when the image contains just the object.
The first order (mean), the second (variance) and the third order (skewness) color moments have been proved to be efficient and effective in representing color distributions of images. Every image inserted to the collection is analyzed to estimates a color histogram which defines the quantity of pixels of color within the image. Color histogram of an image is a description of the colors present in an image and in what quantities. They are computationally efficient to compute and insensitive to small perturbations in camera position. The Color Structure Descriptor represents an image by both the color distribution of the image and the local spatial structure of the color.
One of the main aspects of color feature extraction is the choice of a color space. A color space is a multidimensional space in which the different dimensions represent the different components of color . Most color spaces are three dimensional. Example of a color space is RGB, which assigns to each pixel a three element vector giving the color intensities of the three primary colors, red, green and blue. The space spanned by the R, G, and B values completely describes visible colors, which are represented as vectors in the 3D RGB color space. Retrieving images based on colour similarity is achieved by computing a colour histogram for individual image that visualize the quantity of pixels within an image holding particular values.
Shape  may be defined as the characteristic surface configuration of an object; an outline or contour. It shows that objects are mostly familiar by their shape. Shape feature alone provides capability to recognize objects and retrieve similar images on the basis of their contents. A number of features qualities of object shape are estimated for every object recognized in the stored image. Queries for shape retrieval to be defined by giving an example for each image to act as the query. Retrieving those stored images whose features are closely matches with that particular query image. Shape feature are commonly defined in two ways – global features which means aspect ratio, circularity and moment invariants and local features such as group of consecutive boundary.
Li and Ma  discussed that the geometric moment’s method or region-based and the fourier descriptor or boundary-based were related by a simple linear transformation. Babu et al.  compared the performance of boundary-based representations such as chain code, Fourier descriptor and UNL Fourier descriptor, region-based representations such as moment invariants, Zernike moments and pseudo-Zernike moments and combined representations such as moment invariants and Fourier descriptor, moment invariants and UNL Fourier descriptor. Their experiments showed that the combined representations outperformed the simple representations.
Image texture  is a widely used and primitive visual feature of an image. Texture feature plays important role to separate regions. It refers to the visual patterns that have property of homogeneity or arrangement that do not result from the presence of only a single colour or intensity. This is widely used because it is based on human texture representation. Various texture representations have been investigated in both pattern recognition and computer vision. It focuses property of nearly all surfaces such as clouds, trees, bricks, hair, and fabric. Textures are represented by texels which are then fixed into a many sets, based on how many textures which are detected in the image. It not only defines the texture, but also defines the image in which where the texture is located.
The six texture properties were coarse, contrast, directional, line likeness, regularity and roughness. The most common measures for capturing the images are wavelets and Gabor filters. Which try to retrieve the image or image parts characteristics with reference to the changes in certain directions and the scale of the images. This is most useful for region or images with homogeneous texture.
Examples for texture
Texture is a difficult concept to visualize. The specific textures in an image are defined primarily by modelling the texture as a two-dimensional gray level variation. The accurate brightness of set of pixels is estimated such that degree of contrast, regularity, coarseness and directionality may be estimated. It matches texture regions in images to words representing texture attributes.
ii. Similarity Measures
This involves matching these features to yield a result that is visually similar. Instead of exact matching, content-based image retrieval calculates visual similarities between a query image and images in a huge database. The result is not a single image but a list of images ranked by their similarities based on the query image. Many similarity measures have been developed for image retrieval based on empirical estimates of the distribution of features in recent years. The commonly used similarity measure method is the Distance method. Different similarity/distance measures will affect retrieval performances of an image retrieval system significantly.
Similarity measures for color features are- Histogram Quadratic Distance Measure, Integrated Histogram Bin Matching, Histogram intersection, Histogram Euclidean distance, Minkowskimetric, Manhattan distance, Canberra distance, Angular distance, czekanonski coefficient, Inner product, Dice coefficient, Cosine coefficient, Jaccard coefficient,.
Similarity measurement for texture features are- Kull back-leiber distance, Tree structured wavelet transform, Generalized Gaussian density, Histogram method, wavelet transform, Pyramid structured wavelet transform, Multiresolution simultaneous autoregressive model ,weighted Euclidean distance, Monte-Carlo method and Earth movers distance shows higher accuracy and flexibility in focusing texture information.
Similarity measurement for shape features are- Perceptual distance, Polygon approximation method, Fourier descriptor method, Time Wrapping, Angular distance, Inner product, Dice coefficient, Ray distance and Ordinal co-relation. DTW develop a comfortable distance calculation scheme which is enough with the human visual system in perceiving shape similarity.
In this paper, we will discuss about Image retrieval and also discussed in text and content-based image retrieval. Text based image retrieval has some limitations like the task of describing image content is highly subjective. So overcomes this problem, we will discuss the CBIR system. CBIR is a fast developing technology with considerable potential. Research in CBIR has been focused on image processing, low level feature extraction and so on. It has been believed that CBIR provides maximum support in bridging ‘semantic gap’ between low level feature and richness of human semantics. Feature extraction is the process of extracting image features to a distinguishable extent. CBIR system distinguishes the different regions present in an image based on their similarity in colour, texture and shape. CBIR technology has been used in several applications such as fingerprint identification, biodiversity, digital libraries, crime prevention, medicine, historical research. Similarity measures are used to determine how similar or dissimilar in the given query image and image database collections. In this paper we focused on the study of content based image retrieval and future enhancement is to implement the content based image retrieval in medical field.
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