Similarity of color images.

"... This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image fea ..."

Abstract - Cited by 507 (15 self) - Add to MetaCart

This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C ○ 1999 Academic Press 1.

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A metric for distributions with applications to image databases

by Yossi Rubner, Carlo Tomasi, Leonidas J. Guibas , 1998

"... We introduce a new distance between two distributions that we call the Earth Mover’s Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distributioninto the other by moving “distribution mass ” around. This is a special case of the transportation proble ..."

Abstract - Cited by 438 (6 self) - Add to MetaCart

We introduce a new distance between two distributions that we call the Earth Mover’s Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distributioninto the other by moving “distribution mass ” around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search. 1

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Combining fuzzy information from multiple systems (Extended Abstract)

by Ronald Fagin , 1996

"... In a traditional database system, the result of a query is a set of values (those values that satisfy the query). In other data servers, such as a system with queries baaed on image content, or many text retrieval systems, the result of a query is a sorted list. For example, in the case of a system ..."

Abstract - Cited by 417 (5 self) - Add to MetaCart

In a traditional database system, the result of a query is a set of values (those values that satisfy the query). In other data servers, such as a system with queries baaed on image content, or many text retrieval systems, the result of a query is a sorted list. For example, in the case of a system with queries based on image content, the query might aak for objects that are a particular shade of red, and the result of the query would be a sorted list of objects in the database, sorted by how well the color of the object matches that given in the query. A multimedia system must somehow synthesize both types of queries (those whose result is a set, and those whose result is a sorted list) in a consistent manner. In this paper we discuss the solution adopted by Garlic, a multimedia information system being developed at

NeTra: A toolbox for navigating large image databases

by Wei-ying Ma, B.S. Manjunath - Multimedia Systems , 1999

"... . We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robu ..."

Abstract - Cited by 382 (15 self) - Add to MetaCart

. We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the colo...

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Chabot: Retrieval from a Relational Database of Images

by Virginia E. Ogle, Michael Stonebraker , 1995

"... Chabot is a picture retrieval system for a database that will eventually include over 500,000 digitized multi-resolution images. We describe the design and construction of this system which uses the relational database management system POSTGRES for storing and managing the images and their associat ..."

Abstract - Cited by 299 (1 self) - Add to MetaCart

Chabot is a picture retrieval system for a database that will eventually include over 500,000 digitized multi-resolution images. We describe the design and construction of this system which uses the relational database management system POSTGRES for storing and managing the images and their associated textual data. For retrieval, Chabot uses tools provided by POSTGRES, such as representation of complex data types, a rich query language, and extensible types and functions. To implement retrieval from the current collection of 11,643 images, Chabot integrates the use of stored text and other data types with content-based analysis of the images to perform "concept queries". 1. Introduction The Chabot project was initiated at UC Berkeley to study storage and retrieval from a large collection of digitized images. The images we use belong to the State of California Department of Water Resources (DWR), the agency that oversees the system of reservoirs, aqueducts and water pumping stations th...

On the Removal of Shadows from Images

by Graham D. Finlayson, Steven D. Hordley, Cheng Lu, Mark S. Drew , 2006

"... This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequenc ..."

Abstract - Cited by 236 (18 self) - Add to MetaCart

This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then extend this 1D representation to an equivalent 2D, chromaticity representation. We show that in this 2D representation, it is possible to relight all the image pixels in the same way, effectively deriving a 2D image representation which is additionally shadow-free. Finally, we show how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges. We then remove shadow edges from the edge-map of the original image by edge in-painting and we propose a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow-free image.

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Supervised learning of semantic classes for image annotation and retrieval

by Gustavo Carneiro, Antoni B. Chan, Pedro J. Moreno, Nuno Vasconcelos - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007

"... Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to- ..."

Abstract - Cited by 223 (18 self) - Add to MetaCart

Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning. Index Terms—Content-based image retrieval, semantic image annotation and retrieval, weakly supervised learning, multiple instance learning, Gaussian mixtures, expectation-maximization, image segmentation, object recognition. 1

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Content-based representation and retrieval of visual media: A state-of-the-art review

by Hongjiang Zhang, Dragutin Petkovic - Multimedia Tools and Applications , 1996

"... This paper reviews a number of recently available techniques in contentanalysis of visual media and their application to the indexing, retrieval,abstracting, relevance assessment, interactive perception, annotation and re-use of visualdocuments. 1. Background A few years ago, the problems of represe ..."

Abstract - Cited by 161 (3 self) - Add to MetaCart

This paper reviews a number of recently available techniques in contentanalysis of visual media and their application to the indexing, retrieval,abstracting, relevance assessment, interactive perception, annotation and re-use of visualdocuments. 1. Background A few years ago, the problems of representation and retrieval of visualmedia were confined to specialized image databases (geographical, medical, pilot experimentsin computerized slide libraries), in the professional applications of the audiovisualindustries (production, broadcasting and archives), and in computerized training or education. The presentdevelopment of multimedia technology and information highways has put content processing of visualmedia at the core of key application domains: digital and interactive video, large distributed digital libraries, multimedia publishing. Though the most important investments have been targeted at the information infrastructure (networks, servers, coding and compression, deliverymodels, multimedia systems architecture), a growing number of researchers have realized thatcontent processing will be a key asset in putting together successful applications. The need for contentprocessing techniques has been made evident from a variety of angles, ranging from achievingbetter quality in compression, allowing user choice of programs in video-on-demand, achieving betterproductivity in video production, providing access to large still image databases or integrating still images and video in multimedia publishing and cooperative work. Content-based retrieval of visual media and representation of visualdocuments in human-computer interfaces are based on the availability of content representationdata (time-structure for

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