Content-based query of image databases: inspirations from text retrieval

Squire, David; Müller, Wolfgang; Müller, Henning; Pun, Thierry

doi:10.1016/s0167-8655(00)00081-7

Cited by 130 publications

(55 citation statements)

References 19 publications

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“…A technique that uses a proximity measure to decide which tree nodes can be pruned, even if their bounding regions overlap the query region, is proposed in [22]. One of the early works that suggested the use of inverted index for Content-Based Information Retrieval (CBIR) is the Viper system [23], in which images are indexed by a huge number of visual features that can either be present or absent in each image, as words in a text document. The approach used in this paper [24] is a hybrid approach since it uses a space transformation, while it also reduces the number of object comparisons needed during the indexing and search.…”

Section: Background and Related Workmentioning

confidence: 99%

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Lazaridis

Αξενόπουλος

Rafailidis

et al. 2013

Signal Processing: Image Communication

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In this paper, a novel framework for multimodal search and retrieval of rich media objects is presented. The searchable items are media representations consisting of multiple modalities, such as 2D images, 3D objects and audio files, which share a common semantic concept. A manifold learning technique based on Laplacian Eigenmaps was appropriately modified in order to merge the low-level descriptors of each separate modality and create a new low-dimensional multimodal feature space, where all media objects can be mapped irrespective of their constituting modalities. To accelerate search and retrieval and make the framework suitable even for web-scale applications, a multimedia indexing scheme is adopted, which represents each object of the dataset by the ordering of a number of reference objects. Moreover, the hubness property is introduced in this paper as a criterion to select the most representative reference objects, thus, achieving the maximum possible performance of indexing. The content-based similarity of the multimodal descriptors is also used to automatically annotate the objects of the dataset using a predefined set of attributes. Annotation propagation is utilized to approximate the multimodal descriptors for multimodal queries that do not belong to the dataset.

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Section: Background and Related Workmentioning

confidence: 99%

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Lazaridis

Αξενόπουλος

Rafailidis

et al. 2013

Signal Processing: Image Communication

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“…This system unfortunately did not receive much use as the implementation of a MRML framework would result in additional work for the researchers. The framework also had the disadvantage that the database was fixed (it could of course be extended to several data sets); that due to little use there were few comparisons with state of the art techniques; and that mainly the GNU Image Finding Tool (GIFT 4 , [14]) that implements MRML natively was the baseline.…”

Section: Review Of Web-based and Component-level Evaluationmentioning

confidence: 99%

Automated Component–Level Evaluation: Present and Future

Hanbury

Müller

2010

Lecture Notes in Computer Science

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Abstract.Automated component-level evaluation of information retrieval (IR) is the main focus of this paper. We present a review of the current state of web-based and component-level evaluation. Based on these systems, propositions are made for a comprehensive framework for web service-based component-level IR system evaluation. The advantages of such an approach are considered, as well as the requirements for implementing it. Acceptance of such systems by researchers who develop components and systems is crucial for having an impact and requires that a clear benefit is demonstrated.

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“…These are features which can be precalculated for each individual image, and furthermore can also be stored in an inverted file, for faster lookup [15].…”

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confidence: 99%

Image Cluster Compression Using Partitioned Iterated Function Systems and Efficient Inter-image Similarity Features

Kramm

2007

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System

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Abstract-When dealing with large scale image archive systems, efficient data compression is crucial for the economic storage of data. Currently, most image compression algorithms only work on a per-picture basis -however most image databases (both private and commercial) contain high redundancies between images, especially when a lot of images of the same objects, persons, locations, or made with the same camera, exist. In order to exploit those correlations, it's desirable to apply image compression not only to individual images, but also to groups of images, in order to gain better compression rates by exploiting inter-image redundancies. This paper proposes to employ a multi-image fractal Partitioned Iterated Function System (PIFS) for compressing image groups and exploiting correlations between images. In order to partition an image database into optimal groups to be compressed with this algorithm, a number of metrics are derived based on the normalized compression distance (NCD) of the PIFS algorithm. We compare a number of relational and hierarchical clustering algorithms based on the said metric. In particular, we show how a reasonable good approximation of optimal image clusters can be obtained by an approximation of the NCD and nCut clustering. While the results in this paper are primarily derived from PIFS, they can also be leveraged against other compression algorithms for image groups.

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Content-based query of image databases: inspirations from text retrieval

Cited by 130 publications

References 19 publications

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Automated Component–Level Evaluation: Present and Future

Image Cluster Compression Using Partitioned Iterated Function Systems and Efficient Inter-image Similarity Features

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