Improving Image Search based on User Created Communities
Amruta Joshi, Junghoo Cho, Dragomir Radev, Ahmed Hassan

TL;DR
This paper introduces a probabilistic model that leverages user-created communities to improve text-based image search by better capturing the intended meaning behind tags and queries.
Contribution
It proposes a novel community-based concept inference method that enhances image search effectiveness over traditional cluster-based approaches.
Findings
Community-based approach outperforms cluster-based method
Concept-driven search significantly improves retrieval accuracy
User communities provide more intuitive concept representations
Abstract
Tag-based retrieval of multimedia content is a difficult problem, not only because of the shorter length of tags associated with images and videos, but also due to mismatch in the terminologies used by searcher and content creator. To alleviate this problem, we propose a simple concept-driven probabilistic model for improving text-based rich-media search. While our approach is similar to existing topic-based retrieval and cluster-based language modeling work, there are two important differences: (1) our proposed model considers not only the query-generation likelihood from cluster, but explicitly accounts for the overall "popularity" of the cluster or underlying concept, and (2) we explore the possibility of inferring the likely concept relevant to a rich-media content through the user-created communities that the content belongs to. We implement two methods of concept extraction: a…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
