The CLEF Cross Language Image Retrieval Track (ImageCLEF) 2004

Clough, Paul; Sanderson, Mark; Müller, Henning

doi:10.1007/978-3-540-27814-6_31

Cited by 48 publications

(40 citation statements)

References 18 publications

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“…As a benchmark project, ImageCLEF is more and more wellknown with its open data platform [12]. And the database used in our experiments is coming from ImageCLEF 2005.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

An Automatic Classification System Applied in Medical Images

Qiu

2006

2006 IEEE International Conference on Multimedia and Expo

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In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: Principle Component Analysis (PCA) and Supporting Vector Machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the middle-level blob feature and low-level features (down-scaled images and their texture maps) achieved the highest recognition accuracy. Using the 9000 given training images from ImageCLEF05, our proposed method has achieved a recognition rate of 88.9% in a simulation experiment. And according to the evaluation result from the ImageCLEF05 organizer, our method has achieved a recognition rate of 82% over its 1000 testing images.

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“…As a benchmark project, ImageCLEF is more and more wellknown with its open data platform [12]. And the database used in our experiments is coming from ImageCLEF 2005.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

An Automatic Classification System Applied in Medical Images

Qiu

2006

2006 IEEE International Conference on Multimedia and Expo

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“…the set of documents to be searched is known prior to retrieval, but the search requests are not Given these general categories (and others), topics were created by refinement based on attributes such as name of photographer, date and location. A list of topic titles can be found in [13]. These are typical of retrieval requests from picture archives where semantic knowledge is required in addition to the image itself to perform retrieval.…”

Section: Ad Hoc Retrieval From the St Andrews Collectionmentioning

confidence: 99%

“…This resulted in 30-35 images being chosen. One of the authors then used these images for query-by-example searches to find further images in the database resembling the query using feedback and the case notes and selected 26 of these for the final topic set (see [14], [13]). Similar to the ad hoc task, participants were free to use any method for retrieval, but were asked to identify their runs against three main query dimensions: with and without relevance feedback, visual vs. visual+text, and manual vs. automatic.…”

Section: Medical Retrieval From Casimagementioning

confidence: 99%

The CLEF 2005 Cross–Language Image Retrieval Track

Clough

Müller

Deselaers

et al. 2006

Lecture Notes in Computer Science

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Abstract. The purpose of this paper is to outline efforts from the 2004 CLEF cross-language image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Three tasks were offered in the ImageCLEF track: a TREC-style ad-hoc retrieval task, retrieval from a medical collection, and a user-centered (interactive) evaluation task. Eighteen research groups from a variety of backgrounds and nationalities participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main findings.

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“…The St Andrew's documents are composed of images and text annotations as described in [3]. The search topics are similarly composed of a search image and text description.…”

Section: Text and Image Combination Runsmentioning

confidence: 99%

Dublin City University at CLEF 2004: Experiments with the ImageCLEF St. Andrew’s Collection

Jones

Groves

Khasin

et al. 2005

Multilingual Information Access for Text, Speech and Images

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Abstract. For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness.

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The CLEF Cross Language Image Retrieval Track (ImageCLEF) 2004

Cited by 48 publications

References 18 publications

An Automatic Classification System Applied in Medical Images

An Automatic Classification System Applied in Medical Images

The CLEF 2005 Cross–Language Image Retrieval Track

Dublin City University at CLEF 2004: Experiments with the ImageCLEF St. Andrew’s Collection

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