Progressive search and retrieval in large image archives

Castelli, Vittorio; Bergman, L.; Kontoyiannis, Ioannis; Li, C.-S.; Robinson, John T.; Turek, John

doi:10.1147/rd.422.0253

Cited by 34 publications

(10 citation statements)

References 24 publications

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“…The run was not submitted to the challenge, but given its results it would have ranked 26th among 28 real submissions. Güld et al (2006) used the texture features proposed in Tamura et al (1978) and Castelli et al (1998) image. They also used a down-scaled representation of the original images evaluating the cross correlation function and the image distortion model value as distance measure.…”

Section: Introductionmentioning

confidence: 99%

Discriminative cue integration for medical image annotation

Tommasi

Orabona

Caputo

2008

Pattern Recognition Letters

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a b s t r a c tAutomatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a multi-cue approach to automatic medical image annotation. We represent images using global and local features. These cues are then combined using three alternative approaches, all based on the support vector machine algorithm. We tested our methods on the IRMA database, and with two of the three approaches proposed here we participated in the 2007 ImageCLEFmed benchmark evaluation, in the medical image annotation track. These algorithms ranked first and fifth, respectively among all submission. Experiments using the third approach also confirm the power of cue integration for this task.

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Section: Introductionmentioning

confidence: 99%

Discriminative cue integration for medical image annotation

Tommasi

Orabona

Caputo

2008

Pattern Recognition Letters

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“…The content of a medical image is represented by texture features proposed by Tamura et al [3] and Castelli et al [4], denoted as TTM and CTM, respectively. Down-scaled representations of the original images were computed to 32 × 32 and X × 32, pixels disregarding and according to the original aspect ratio, respectively.…”

Section: Image Featuresmentioning

confidence: 99%

Baseline Results for the ImageCLEF 2006 Medical Automatic Annotation Task

Güld

Thies

Fischer

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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The ImageCLEF 2006 medical automatic annotation task encompasses 11,000 images from 116 categories, compared to 57 categories for 10,000 images of the similar task in 2005. As a baseline for comparison, a run using the same classifiers with the identical parameterization as in 2005 is submitted. In addition, the parameterization of the classifier was optimized according to the 9,000/1,000 split of the 2006 training data. In particular, texture-based classifiers are parallel combined with classifiers, which use spatial intensity information to model common variabilities among medical images. However, all individual classifiers are based on global features, i.e. one feature vector describes the entire image. The parameterization from 2005 yields an error rate of 21.7%, which ranks 13 th among the 28 submissions. The optimized classifier yields 21.4% error rate (rank 12), which is insignificantly better.

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“…Some promising researches have been led by Castelli [12], Jaimes [13], and Hong [14] who suggested the use of a hierarchical structure in order to reach progressive image analysis. According to their method, the contents of images can be represented on different levels, from the three-level representation up to five-level representation introduced by Jaimes [13] in 1999.…”

Section: Introductionmentioning

confidence: 99%

Object extraction as a basic process for content-based image retrieval (CBIR) system

Jaworska

2007

Opto-Electronics Review

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This article describes the way in which image is prepared for content-based image retrieval system. Automated image extraction is crucial; especially, if we take into consideration the fact that the feature selection is still a task performed by human domain experts and represents a major stumbling block in the process of creating fully autonomous CBIR systems. Our CBIR system is dedicated to support estate agents. In the database, there are images of houses and bungalows. We put all our efforts into extracting elements from an image and finding their characteristic features in the unsupervised way. Hence, the paper presents segmentation algorithm based on a pixel colour in RGB colour space. Next, it presents the method of object extraction applied to obtain separate objects prepared for the process of introducing them into database and further recognition. Moreover, we present a novel method of texture identification which is based on wavelet transformation. Due to the fact that the majority of texture is geometrical (such as bricks and tiles) we have used the Haar wavelet. After a set of low-level features for all objects is computed, the database is stored with these features.

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Progressive search and retrieval in large image archives

Cited by 34 publications

References 24 publications

Discriminative cue integration for medical image annotation

Discriminative cue integration for medical image annotation

Baseline Results for the ImageCLEF 2006 Medical Automatic Annotation Task

Object extraction as a basic process for content-based image retrieval (CBIR) system

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