We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

https://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/rui99_cbir_survey.pdf

Image Retrieval

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

/pdf/qbic-project-querying-images-by-content-using-color-texture-3jgprbildi.pdf

QBIC project: querying images by content, using color, texture, and shape

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

/pdf/simplicity-semantics-sensitive-integrated-matching-for-3bfwe17qcj.pdf

SIMPLIcity: semantics-sensitive integrated matching for picture libraries

Review of shape representation and description techniques

Query by Visual Example - Content based Image Retrieval

Search engines are useful because they allow the user to find information of interest from the World Wide Web. However, most of the popular search engines today are textuals they do not allow the user to find images from the Web. This paper describes AMORE, a Web search engine that allows the user to retrieve images from the Web by specifying relevant keywords or a similar image. Text and image search can also be combined. Moreover, we have developed a Query Result Visualization Environment that allows the organization of the results if many images are retrieved. In this paper we present AMORE’s user interface and explain the technique for retrieving images visually similar to a user specified image. The method of automatically assigning relevant keywords to the images is then explained. Finally, the architecture of the system as well as some interesting observations of our experiences with AMORE are discussed.

AMORE: A World Wide Web image retrieval engine

Users of the Web are overloaded with information. This medium is “polluted” with redundant, erroneous and low quality information. A WWW survey of 11,700 users conducted from April 10 to May 10, 1996[1] indicates that 30.31% of the users report “finding known info” is their problem and 27.80% of the users report organizing collected information as their problem. An empirical study[2] on users’ revisitation patterns to WWW pages found that 58% of an individual’s pages are revisits. With these study results, we believe the Web users would like to build and organize a larger collection of bookmarks for future references than they can reasonably maintain now.

PowerBookmarks: a system for personalizable Web information organization, sharing, and management

A method and apparatus for querying a database of images, in which the results of the query are based upon a combination of region based image matching and boundary based image matching. Additionally similarity calculations are made between individual results of the query so that the results may be clustered based upon similarity.

Method and system for image querying using region based and boundary based image matching

Search engines are useful because they allow the user to find information of interest from the World-Wide Web. However, most of the popular search engines today are textual; they do not allow the user to find images from the Web. This paper describes a search engine that integrates text and image search. One or more Web sites can be indexed for both textual and image information, allowing the user to search based on keywords or images or both. Another problem with the current search engines is that they show the results as pages of scrolled list; this is not very user-friendly. Therefore our search engine allows the user to visualize the results in various ways. This paper explains the indexing and searching techniques of the search engine and highlights several features of the querying interface to make the retrieval process more efficient. Examples are used to show the usefulness of the technology.

Kyoji Hirata

Papers

Query by Visual Example - Content based Image Retrieval

AMORE: A World Wide Web image retrieval engine

PowerBookmarks: a system for personalizable Web information organization, sharing, and management

Method and system for image querying using region based and boundary based image matching

Towards a multimedia World-Wide Web information retrieval engine