Image Retrieval and Annotation Using Maximum Entropy

Deselaers, Thomas; Weyand, Tobias; Ney, Hermann

doi:10.1007/978-3-540-74999-8_91

Cited by 21 publications

(7 citation statements)

References 13 publications

(20 reference statements)

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“…Many approaches have been introduced to extract and analyse image features e.g. image annotation with maximum entropy to learn feature weights ( 52 ), colour-constant-colour-indexing ( 53 ), multi resolution histograms to distinguish between images with respect to the robustness and noise ( 54 ), medical image segmentation using Geodesic Active Contour ( 55 ), analysing image texture with visual information ( 56 ), medical image annotation ( 57 ) etc. The ultimate goal is to process images to identify their commonalities and variabilities for observatory scientific and clinical decisions, e.g.…”

Section: Biomedical Imaging and Information Retrievalmentioning

confidence: 99%

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

2016

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Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.

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Section: Biomedical Imaging and Information Retrievalmentioning

confidence: 99%

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

2016

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“…The independence between features and its automatic weighing has already been studied under the concepts of entropy impurity [2] and maximum entropy [5]. For fulfilling efficiency, the M 3 -tree [3] is a multi-metric index that supports dynamic combinations of metric functions, by storing partial distances of each metric and estimating the weighted distance for discarding groups of objects.…”

Section: Thesis Proposalmentioning

confidence: 99%

Content-based video copy detection

Barrios

2009

Proceedings of the 17th ACM International Conference on Multimedia

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Content-Based Video Copy Detection consists in retrieving all the modified versions of an original document in a video collection. It relies on two tasks: content description, for extracting one or many fingerprints from a video document, and similarity search, for determining the set of extracted fingerprints that make a close match.For the similarity search task, a copy detection system usually relies on a metric distance for measuring the degree of similarity between fingerprints. The metric properties represent a tradeoff between efficiency and effectiveness for a similarity search. A metric distance allows the use of well studied indexing structures. However, the metric properties restrict the similarity model that can be used for comparing two objects.For the present thesis, the main focus will be on researching similarity models for video sequences that do not necessarily comply the metric properties. In particular, we plan to research multi-metric and non-metric similarity measures for fulfilling effective and efficient detection. The issues involved in video copy detection (visual transformations, local and global fingerprints, temporal dimension, and approximated searches) make this problem a relevant topic for researching new similarity models.

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“…The major disadvan-tage of this approach is the curse of dimensionality: as the the dimensionality of the feature space increases the density of elements in the space is reduced, scattering meaningful clusters of instances. To solve this problem, various feature selection, feature normalization [46] and feature weighting [36,142] schemes have been used.…”

Section: Early Fusion Approachesmentioning

confidence: 99%

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Foncubierta–Rodríguez

Müller

Depeursinge

2013

Multimed Tools Appl

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Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higherdimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications.

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Image Retrieval and Annotation Using Maximum Entropy

Cited by 21 publications

References 13 publications

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

Content-based video copy detection

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

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