Image Clustering System on WWW using Web Texts

Sunayama, Wataru; Nagata, Akihiko; Yachida, Masahiko

doi:10.1109/ichis.2004.59

Cited by 5 publications

(2 citation statements)

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“…Most of the traditional web image clustering approaches [1], [2], [3] often focus on utilizing individual information to group images. Due to the simplicity of feature extraction and computation, these techniques are still adopted by the majority of web image applications.…”

Section: Related Workmentioning

confidence: 99%

Web Multimedia Object Clustering via Information Fusion

et al. 2011

2011 International Conference on Document Analysis and Recognition

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Multimedia information plays an increasingly important role in humans daily activities. Given a set of web multimedia objects (images with corresponding texts), a challenging problem is how to group these images into several clusters using the available information. Previous researches focus on either adopting individual information, or simply combining image and text information together for clustering. In this paper, we propose a novel approach (Dynamic Weighted Clustering) to separate images under the "supervision" of text descriptions; Also, we provide a comparative experimental investigation on utilizing text and image information to tackle web image clustering. Empirical experiments on a manually collected web multimedia object (related to the events after disasters) dataset are conducted to demonstrate the efficacy of our proposed method.

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Section: Related Workmentioning

confidence: 99%

Web Multimedia Object Clustering via Information Fusion

et al. 2011

2011 International Conference on Document Analysis and Recognition

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“…Functions to calculate features are re-designed and tested to fit well with our model. Some research [3,7] discussed the issues on image clustering. All of them use the text surrounding images as the text feature.…”

Section: Related Workmentioning

confidence: 99%

Image Description Mining and Hierarchical Clustering on Data Records Using HR-Tree

Zhang

Huang

Xue

et al. 2006

Frontiers of WWW Research and Development - APWeb 2006

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Abstract. Since we can hardly get semantics from the low-level features of the image, it is much more difficult to analyze the image than textual information on the Web. Traditionally, textual information around the image is used to represent the high-level features of the image. We argue that such "flat" representation can not describe images well. In this paper, Hierarchical Representation (HR) and HR-Tree are proposed for image description. Salient phrases in HRTree are further to distinguish this image with others sharing the same ancestor concepts. First, we design a method to extract the salient phrases for the images in data records. Then HR-Trees are built using these phrases. Finally, new hierarchical clustering algorithm based on HR-Tree is proposed for users' browsing conveniently. We demonstrate some HR-Trees and clustering results in experimental section.. These results illustrate the advantages of our methods.

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Smart Discussion Environment on a Bulletin Board System

Sunayama

2006

Chance Discoveries in Real World Decision Making

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Image Clustering System on WWW using Web Texts

Cited by 5 publications

References 5 publications

Web Multimedia Object Clustering via Information Fusion

Web Multimedia Object Clustering via Information Fusion

Image Description Mining and Hierarchical Clustering on Data Records Using HR-Tree

Smart Discussion Environment on a Bulletin Board System

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