CINDI at ImageCLEF 2006: Image Retrieval &amp; Annotation Tasks for the General Photographic and Medical Image Collections

Rahman, Md. Mahmudur; Sood, Varun; Desai, Bipin C.; Bhattacharya, Prabir

doi:10.1007/978-3-540-74999-8_90

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…the class of the most similar training image is chosen for every test image. Analogous to feature combination, classifier combination has also been a popular way to improve performance (Rahman et al, 2006;Tommasi et al, 2008b;Avni et al, 2008).…”

Section: Classification Methodsmentioning

confidence: 99%

The Medical Image Classification Task

Tommasi

Deselaers

2010

ImageCLEF

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We describe the medical image classification task in ImageCLEF 2005ImageCLEF -2009 It evolved from a classification task with 57 classes on a total of 10,000 images into a hierarchical classification task with a very large number of potential classes. Here, we describe how the database and the objectives changed over the years and how state-of-the-art approaches from machine learning and computer vision were shown to outperform the nearest neighbor-based classification schemes working on full-image descriptors that were very successful in 2005. In particular the use of discriminative classification methods such as support vector machines and the use of local image descriptors were empirically shown to be important building blocks for medical image classification.

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Section: Classification Methodsmentioning

confidence: 99%

The Medical Image Classification Task

Tommasi

Deselaers

2010

ImageCLEF

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“…The multimedia image retrieval task has been mainly faced with information fusion techniques that attempt to combine visual and textual information (Goodrum 2000;Datta et al 2008;Rui et al 1999;Clough et al 2007;Grubinger et al 2008;Arni et al 2009;Westerveld 2004;Escalante et al 2008aWesterveld et al 2000;Raicu 2006;Escalante et al 2008b;Chua et al 1994;Ishikawa et al 1998;La et al 1998;Sclaroff et al 1999;Elworthy 2000;Inoue and Ueda 2005;Hoi et al 2005;Peinado et al 2005;Jones and McDonald 2005;Martínez-Fernández et al 2005Adriani and Framadhan 2005;Izquierdo-Beviá et al 2005;Chang et al 2005;Martín-Valdivia et al 2005;Besancon and Millet 2006;Chang and Chen 2006;Curtoni 2006;Maillot et al 2006;Rahman et al 2006;Lestari-Paramita et al 2009). Visual information is incorporated through vectors of visual features that are extracted from images, whereas text is incorporated through vectors of occurrence statistics.…”

Section: Related Workmentioning

confidence: 98%

Multimodal indexing based on semantic cohesion for image retrieval

Montes

Sucar

2011

Inf Retrieval

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This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a mid-level description of the image (i.e., in terms of the objects that can be seen in the image). Accordingly, the main assumption of this work is that by combining information from text and labels we can develop very effective retrieval methods. We study standard information fusion techniques for combining both sources of information. However, whereas the performance of such techniques is highly competitive, they cannot capture effectively the content of images. Therefore, we propose two novel representations for multimodal images that attempt to exploit the semantic cohesion among terms from different modalities. Such representations are based on distributional term representations widely used in computational linguistics. Under the considered representations the content of an image is modeled by a distribution of co-occurrences over terms or of occurrences over other images, in such a way that the representation can be considered an expansion of the multimodal terms in the image. We report experimental results using the SAIAPR TC12 benchmark on two sets of topics used in ImageCLEF competitions with manually and automatically generated labels. Experimental results show that the proposed representations outperform significantly both, standard multimodal techniques and unimodal methods. Results on manually assigned labels provide an upper bound in the retrieval performance that can be obtained, whereas results with automatically generated labels are encouraging. The novel representations are able to capture more effectively the content of multimodal images. We emphasize that although we have applied our representations to multimedia image retrieval the same formulation can be adopted for modeling other multimodal documents (e.g., videos).

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“…Các hàm tìm kiếm được xây dựng để tra cứu theo sự quan tâm. Bài báo này sử dụng kết hợp nhiều biểu diễn đặc trưng được miêu tả như trong [2, 5,7,9,22,23,24,26]. Trong xếp hạng các kết quả trả về cho người dùng thông thường sử dụng khoảng cách toàn cục bằng kết hợp tuyến tính khoảng cách cục bộ theo biểu diễn đặc trưng thành phần.…”

Section: Giới Thiệuunclassified

Tra cứu ảnh theo nội dung sử dụng tập Pareto và mô hình học thống kê CART

Hiệu¹,

Thắng²,

Quỳnh³

et al. 2016

Công nghệ CNTT-TT

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Image retrieval systems adopt a combination of multiple features and then total distance measures of particular features for ranking the results. Therefore, the top-ranked images with smallest total distance measures are returned to the users. However, images with smallest partial distance measures which are suitable for users’ purpose may not be included in these results. Therefore, partial distance measure should be considered. In this paper, we propose to adopt the Pareto set in the distance measure space. This set assures that the returned results contain not only points with smallest total distance obtained by linear combinations, but also other points have smallest partial distance measures which cannot be found by the linear combination in the distance measure space. Especially, the searching space based on the distance measures is compacted by our algorithm, namely PDFA. This algorithm collects all the Pareto set with different depths, and is efficient for the classification and regression tree (CART). The experimental results on three image collections show the effectiveness of our proposed method.

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CINDI at ImageCLEF 2006: Image Retrieval & Annotation Tasks for the General Photographic and Medical Image Collections

Cited by 9 publications

References 5 publications

The Medical Image Classification Task

The Medical Image Classification Task

Multimodal indexing based on semantic cohesion for image retrieval

Tra cứu ảnh theo nội dung sử dụng tập Pareto và mô hình học thống kê CART

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