Publication Type
Book Chapter
Version
acceptedVersion
Publication Date
5-2019
Abstract
Due to the problem of semantic gap, i.e. the visual content of an image may not represent its semantics well, existing efforts on web image organization usually transform this task to clustering the surrounding text. However, because the surrounding text is usually short and the words therein usually appear only once, existing text clustering algorithms can hardly use the statistical information for image representation and may achieve downgraded performance with higher computational cost caused by learning from noisy tags. This chapter presents using the Probabilistic ART with user preference architecture, as introduced in Sects. 3.5 and 3.4, for personalized web image organization. This fused algorithm is named Probabilistic Fusion ART (PF-ART), which groups images of similar semantics together and simultaneously mines the key tags/topics of individual clusters.Moreover, it performs semi-supervised learning using the user-provided taggings for images to give users direct control of the generated clusters. An agglomerative merging strategy is further used to organize the clusters into a hierarchy, which is of a multi-branch tree structure rather than a binary tree generated by traditional hierarchical clustering algorithms. The entire two-step algorithm is called Personalized Hierarchical Theme-based Clustering (PHTC), for tag-based web image organization. Two large-scale real-world web image collections, namely the NUS-WIDE and the Flickr datasets, are used to evaluate PHTC and compare it with existing algorithms in terms of clustering performance and time cost.
Discipline
Databases and Information Systems | Social Media
Research Areas
Data Science and Engineering
Publication
Adaptive resonance theory in social media data clustering: Roles, methodologies, and applications
Editor
L. Meng, A. H. Tan, & D. C. Wunsch
First Page
93
Last Page
110
ISBN
9783030029845
Identifier
10.1007/978-3-030-02985-2_4
Publisher
Springer
City or Country
Cham
Embargo Period
12-17-2024
Citation
MENG, Lei; TAN, Ah-hwee; and WUNSCH, Donald C..
Personalized web image organization. (2019). Adaptive resonance theory in social media data clustering: Roles, methodologies, and applications. 93-110.
Available at: https://ink.library.smu.edu.sg/sis_research/9811
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-02985-2_4