Web Image Clustering with Reduced Keywords and Weighted Bipartite Spectral Graph Partitioning

@inproceedings{Koh2006WebIC,
  title={Web Image Clustering with Reduced Keywords and Weighted Bipartite Spectral Graph Partitioning},
  author={Su Ming Koh and L. Chia},
  booktitle={PCM},
  year={2006}
}
There has been recent work done in the area of search result organization for image retrieval. The main aim is to cluster the search results into semantically meaningful groups. A number of works benefited from the use of the bipartite spectral graph partitioning method [3][4]. However, the previous works mentioned use a set of keywords for each corresponding image. This will cause the bipartite spectral graph to have a high number of vertices and thus high in complexity. There is also a lack… 

Image clustering through community detection on hybrid image similarity graphs

TLDR
A community detection (i.e. graph-based clustering) approach that makes use of both visual and tagging features of images in order to efficiently extract groups of related images within large image collections.

Tagged Documents Co-Clustering

TLDR
A hierarchical agglomerative co-clustering algorithm is proposed to group together the most related tags into clusters, and some stopping criterion for selectecting an optimal partitioning is proposed.

Study of Cloud Services Recommendation Model Based on Chord Ring

TLDR
Experimental results show that the cloud services recommendation model based on chord ring can effectively improve the recommendation accuracy and recommend efficiency.

References

SHOWING 1-10 OF 13 REFERENCES

Hierarchical clustering of WWW image search results using visual, textual and link information

TLDR
A hierarchical clustering method using visual, textual and link analysis to cluster the search results into different semantic clusters of image search results is proposed.

Web image clustering by consistent utilization of visual features and surrounding texts

TLDR
A novel method named consistent bipartite graph co-partitioning is proposed, which can cluster Web images based on the consistent fusion of the information contained in both low-level features and surrounding texts and can be efficiently solved by semi-definite programming (SDP).

Content-based image retrieval by clustering

TLDR
A novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space.

Iteratively clustering web images based on link and attribute reinforcements

TLDR
A reinforcement clustering framework that reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links to promise the discovery of the semantic structure of images, which is the basis of image clustering.

Frequent term-based text clustering

TLDR
Two algorithms for frequent term-based text clustering are presented, FTC which creates flat clusterings and HFTC for hierarchical clustering, which obtain clusterings of comparable quality significantly more efficiently than state-of-the- artText clustering algorithms.

Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure

TLDR
The region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns to increase computational efficiency while maintaining spatial connectivity in merging.

Using latent semantic analysis to improve access to textual information

TLDR
Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.

Text document clustering based on frequent word sequences

TLDR
In this paper, a new text clustering algorithm, named Clustering based on Frequent Word Sequences (CFWS) is proposed, and it has been shown that CFWS has much better performance.

TCBLHT: a new method of hierarchical text clustering

  • Jian-Suo XuLi Wang
  • Computer Science
    2005 International Conference on Machine Learning and Cybernetics
  • 2005
TLDR
The TCBLHT method can automatically achieve hierarchical text clustering, and establishes vector space model (VSM) of term weight by using the theory of LSA, then semantic relation is included in thevector space model.

An algorithm for suffix stripping

TLDR
An algorithm for suffix stripping is described, which has been implemented as a short, fast program in BCPL and performs slightly better than a much more elaborate system with which it has been compared.