Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

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Content-based image retrieval at the end of the early years

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

You are what you can access: Sharing and collaborative consumption online

A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise—that the appearance of a topic in a document stream is signaled by a “burst of activity,” with certain features rising sharply in frequency as the topic emerges.

The goal of the present work is to develop a formal approach for modeling such “bursts,” in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content. The approach is based on modeling the stream using an infinite-state automaton, in which bursts appear naturally as state transitionss it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic. The resulting algorithms are highly efficient, and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream. Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them.

Bursty and Hierarchical Structure in Streams

A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.The goal of the present work is to develop a formal approach for modeling such "bursts," in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content. The approach is based on modeling the stream using an infinite-state automaton, in which bursts appear naturally as state transitions; in some ways, it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic. The resulting algorithms are highly efficient, and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream. Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them.

Bursty and hierarchical structure in streams

This paper reviews a number of recently available techniques in content analysis of visual media and their application to the indexing, retrieval, abstracting, relevance assessment, interactive perception, annotation and re-use of visual documents.

/pdf/content-based-representation-and-retrieval-of-visual-media-a-2749cu8z10.pdf

Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review

EEcient and reliable methods for the segmentation of the image part of a video into shots have been proposed. In actual motion picture or video documents, there can often be 500 to 1000 shots per hour. Thus, if one wants to enable quick browsing of the video contents, quick positioning in the document for interactive viewing, or if one wants to automatically construct abstracts of the document, it is necessary to nd more macroscopic time objects, for instance larger sequences constituting a narrative unit or sharing the same setting. In this paper, we present a method for obtaining automatically such a macro-segmentation in sequences. This method is based on the application of rules expressing the local (in time) clues which are given by the medium contents to enable identiication of more macroscopical changes. We describe how the results from these rules can be combined to obtain a macro-segmentation, and to extract particularly important representative images. We give arguments for the choice of such a general purpose medium-based approach compared to approaches based on modelling speciic types of contents, and present results from its automatic application to a limited sample of documents. This research was conducted under support from the french Minist ere de la Culture et de la Francophonie.

Medium knowledge-based macro-segmentation of video into sequences

The automatic real-time analysis of film editing and transition effects and its applications

Voor sommigen staat delen gelijk aan piraterij: iets wat moet worden bestreden Anderen zien het als deel van het moderne leven en proberen de schadelijke effecten zoveel mogelijk te compenseren Ondertussen wordt er weinig vooruitgang geboekt in het aanpakken van de echte problemen waar- mee de cultuur te maken krijgt wanneer steeds meer mensen kunnen bijdragen aan het produceren van cultureel waardevolle werken Sharing stelt dat het niet-commercieel delen van digitale werken zowel legitiem als nuttig is, omdat het leidt tot meer diversiteit in de digitale culturele wereld Sharing bespreekt nieuwe financieringsregelingen die geschikt zijn voor een digitale culturele sfeer waar werken vrijelijk gedeeld kunnen worden door individuen Voor meer informatie over het boek, bezoek: wwwsharing-thebooknet

Philippe Aigrain

Papers

Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review

Medium knowledge-based macro-segmentation of video into sequences

The automatic real-time analysis of film editing and transition effects and its applications

Sharing: Culture and the Economy in the Internet Age

Cause commune : l'information entre bien commun et propriété