Image Retrieval

Image Retrieval

Length: 3064 words (8.8 double-spaced pages)

Rating: Excellent

Open Document

Essay Preview

More ↓
I. INTRODUCTION
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 [1]. A massive volume of image data such as digital photographs, medical images and satellite images are generated every day [2].
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 [3] 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.

How to Cite this Page

MLA Citation:
"Image Retrieval." 123HelpMe.com. 27 Feb 2020
    <https://www.123helpme.com/view.asp?id=264378>.

Need Writing Help?

Get feedback on grammar, clarity, concision and logic instantly.

Check your paper »

Image Retrieval Systems Essay examples

- ... In these systems, there exists a tradeoff between accuracy and computational cost. This tradeoff decreases as more efficient algorithms are utilized and increased computational power thus making it inexpensive. Moreover, the existing systems produce efficient results with small and medium sized image databases, but are generally ill-suited when applied to large sized image databases. This research work designs and proposes techniques to improve the process of image retrieval from large databases in terms of accuracy and speed....   [tags: multimedia, searching, indexing]

Research Papers
722 words (2.1 pages)

Image Retrieval System Essay

- An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. A robust natural, geographic and medical image retrieval using a supervised classifier which concentrates on extracted features is proposed. Gray level co-occurrence matrix (GLCM),Scale invariant feature technique(SIFT) and moment invariant features are implemented to extract the features from natural images and GLCM and Gabor feature extraction is done on medical images....   [tags: somputer system, browsing, database]

Research Papers
1969 words (5.6 pages)

Content-Based Image Retrieval (CBIR) Essay

- The biggest issue when it comes to creating a system for image retrieval based on image queries is the semantic gap. The semantic gap is defined as “the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation” (Smeulders). What a system can discern from an image can be completely different to what a human user can discern. It can also be the case that two human users assign different descriptions for the same image, adding to the complexity of the problem....   [tags: vision science, human vision system HVS]

Research Papers
1271 words (3.6 pages)

An Image Recognition/Retrieval System: Design And Implementation Essay

- Introduction This paper discusses the design and implementation of an image recognition /retrieval system indexed with parameterized color histogram. As the usage of multimedia data has increased in recent years, effective and efficient methods for storing and retrieving multimedia have been required and are being developed. In particular, images are used as important inputs in a variety of areas. The area of content-based image retrieval is a hybrid research area that requires knowledge of both computer vision and of database systems....   [tags: image recognition, query images, color]

Research Papers
1685 words (4.8 pages)

Essay on Image Search Using Hash Code

- This paper introduces an effective method for query adaptive image retrieval using low-level feature extraction and hashing method. The low-level feature primarily constitute color, shape and texture features. For color feature extraction color moments, color Histogram and color correlogram method were implemented and for texture feature extraction used wavelet moment method. Hashing methods is used to embed high-dimensional image features into Hamming space, where search can be performed in real-time based on Hamming distance of compact hash codes....   [tags: effective method, querry adaptive image retrieval]

Research Papers
1021 words (2.9 pages)

Electronic and Image Records Essay

- Introduction: The chapter we will be looking at today will be discussing electronic and image records. We will focus are time on key terms and key idea throughout of the chapter. The first part of this paper we are going to be look at electronic media; the second part will be record security and safety; and the final part will be image media. Electronic media will be addressing the key ideas; such as relationship between “electronic and image records; magnetic and optical media; removable data storage devices; data input; storage and retrieval procedures.”(342-348) The second part we will look at records safety and security....   [tags: electronic media, security, safety, image media]

Research Papers
1428 words (4.1 pages)

Retrieval Failure in the Long-Term Memory Essay

- This investigation looks at retrieval failure in the long-term memory, particularly context-dependant forgetting. The theory behind retrieval failure is that available information stored in the long-term memory cannot be accessed because the retrieval cues are defective. Cue-dependant forgetting theory focuses on the assumption that the context in which we learn something is significant when we come to recall the information. Recall is better if it takes place in the same context as the learning....   [tags: Papers]

Free Essays
827 words (2.4 pages)

Essay on The Seven Ages of Information Retrieval

- While first reading the article entitled as the seven ages of information retrieval written by Micheal Lesk, it shows that the development of information retrieval is discussed by using the concept of life span produced by the most popular literature, Shakespeare. The author was highlighted the major point used by Shakespeare starting from childhood until retirement to be adapted on the expectation of the article that he has been read before which is the article written by Vennevar Bush in 1945....   [tags: Life Span, Elements]

Research Papers
1634 words (4.7 pages)

Database Mangement and Retrieval System Essay

- Question 1. Differentiate between database management system and information retrieval system by focusing on their functionalities. Database Management System (as known as DBMS) is a set of application that enable user to create, edit, update, store and retrieve data from database files. By using DBMS, data in a database can be added, deleted, changed, sorted and searched. DBMSs are usually used to manage employee information of a big company, customer information and stock information. By using the DBMS, there are advantages and disadvantages....   [tags: Technology, Data]

Research Papers
1417 words (4 pages)

Essay on Information Retrieval

- Michael Lesk adopts Shakespeare’s theory of seven ages of human being which start from infancy to senility to predict the evolution of Information Retrieval from 1945 to 2010. In this paper, Lesk tried to compare two approaches to information retrieval. The first approach is intellectual analysis by human and machine – artificial intelligence introduced by Vannevar Bush’s. The second approach is simple exhaustive processing – statistical detail introduced by Warren Weaver’s .The paper was written in 1995, when the Internet and World Wide Web technology still crawling to grow....   [tags: Intellectual Analysis, Exhaustive Processing]

Research Papers
1286 words (3.7 pages)

A very popular framework of image retrieval was to annotate the images by keyword or text and then use text-based database management systems to operate image retrieval. Two broad surveys on this topic are [4, 5]. Emergence of large-scale image collections in the early 1990s, the major difficulties are manual image annotation is also accurate. To avoid this situation, content-based image retrieval was improved. It means, instead of using manually annotated by text-based key words, images might be indexed by their own visual content such as colour and texture. Since then, many techniques in this research direction have been developed and many image retrieval systems, both research and commercial, have been built. This approach has established a general framework of image retrieval from a new perspective. In this paper we will focus our effort mainly to the content-based image retrieval.
Many content-based image retrieval systems have been recently proposed: Chabot[6], MARS [7], Netra [8], Photobook [9], QBIC [10], Surfimage[12], SWIM [13], Virage [14], Visualseek [15] and WebSeek[16]. 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 [21] 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.

1. Color
The first and most straightforward feature for indexing and retrieving images is color. Color [19] 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 [4]. 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.

2. Shape
Shape [19] 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 [17] 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. [18] 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.

3. Texture
Image texture [19] 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.

V. Conclusion
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.

References
[1] O.R. Zajane, J. Han Z.N. Li and J. Hou, “Mining multimedia data”, Proc. of SIGMOD, 1998.
[2] J. Zhang, H. Wynne, and M. L. Lee, "Image mining: Issues, frameworks, and techniques", Proc. 2nd Int. Workshop Multimedia Data Mining, pp.13 -20, 2001.
[3] T.Karthikeyan, P.Manikandaprabhu, "Function and Information Driven Frameworks for Image Mining - A Review", International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol.2, Issue 11, pp.4202-4206, Nov. 2013.
[4] S.K. Chang and A. Hsu, “Image information systems: Where do we go from here?”, IEEE Trans. on Knowledge and Data Engineering 4(5), 1992.
[5] H. Tamura and N. Yokoya, “Image database systems: A survey”, Pattern Recognition 17(1), 1984.
[6] V. Ogle and M. Stonebraker, “Chabot: Retrieval from a relational database of images”, IEEE Computer, 28(9):40–48, 1995.
[7] S. Mehrotra, Y. Rui, M. Ortega and T. S. Huang, “Supporting content-based queries over images in MARS”, Proc. IEEE Int. Conf. Multimedia Computing Systems, ON, Canada, pp. 632–633, June. 1997.
[8] W. Y. Ma and B. S. Manjunath, “Netra: A toolbox for navigating large image databases”, Proc. IEEE Int. Conf. Image Processing, Santa Barbara, CA, vol. 1, pp. 568–571, Oct. 1997.
[9] A. Pentland, R. W. Picard, and S. Sclaroff, “Photobook: Content-based manipulation of image databases,” Proc. SPIE Storage Retrieval Image Video Databases II, pp. 34–47, Feb. 1994.
[10] C. Faloutsos, R. Barber,M. Flickner, J. Hafner,W. Niblack, D. Petkovic, and W. Equitz, “Efficient and effective querying by image content,” J.Intell. Inform. Syst., vol. 3, pp. 231–262, 1994.
[11] Yong Rui and Thomas S. Huang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues", Journal of Visual Communication and Image Representation 10, 39–62, 1999.
[12] hahab Nastar, Matthias Mitschke, Christophe Meilhac, and Nozha Boujemaa. “Surfimage: A flexible content-based image retrieval system”, Proc. ACM International Multimedia Conference, Bristol, England, pp. 339–344, Sep. 1998.
[13] H. J. Zhang, C. Y. Low, S. W. Smoliar, and J. H. Wu, “Video parsing retrieval and browsing: An integrated and content-based solution”, Proc. ACM Multimedia ’95, San Francisco, CA, pp.15–24, Nov. 1995.
[14] A. Hampapur, A. Gupta, B. Horowitz, C. F. Shu, C. Fuller, J. Bach, M.Gorkani, and R. Jain, “Virage video engine”, Proc. SPIE Storage Retrieval Image Video Databases V, San Jose, CA, pp. 188–197, Feb. 1997.
[15] J. R. Smith and S. F. Chang, “Visualseek: A fully automated content based image query system”, Proc. ACM Multimedia, Boston, MA, pp. 87–98, Nov. 1996.
[16] J. R. Smith and S.F. Chang, Visually searching the web for content, IEEE Multimedia Magazine 4(3), 12–20, 1997. [Columbia U. CU/CTR Technical Report 459-96-25]
[17] B. Li and S. D. Ma, “On the relation between region and contour representation”, Proc. IEEE Int. Conf. on Image Proc., 1995.
[18] B. M. Mehtre, M. Kankanhalli, and W. F. Lee, “Shape measures for content based image retrieval: A comparison”, Information Processing & Management 33(3), 1997.
[19] Ritendra Datta, Dhiraj Joshi, Jia Li, And James Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys, Vol. 40, No. 2, Article 5, April 2008.
[20] A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, 22(12):1349–1380, 2000.
[21] Datta R, Li J, Wang J Z. " Content-based Image Retrieval – Approaches and Trends of the New Age", ACM Intl. Workshop on Multimedia Information Retrieval, Singapore, ACM Multimedia, 2005.
[22] Michael S. Lew, Nicu Sebe, Chabane Djeraba and Ramesh Jain, “Content-based Multimedia Information Retrieval: State of the Art and Challenges”, ACM Transactions on Multimedia Computing, Communications, and Applications, Feb. 2006.

Return to 123HelpMe.com