Finding the Topic of a Set of Images
Gonzalo Vaca-Castano

TL;DR
This paper presents a novel method for identifying the overarching topic of a set of images by leveraging visual similarity, noisy crowd-sourced tags, and a word refinement process, outperforming baseline approaches.
Contribution
The paper introduces a new approach combining image retrieval, word selection via random walks, and a CRF-based word mapping to determine image set topics, handling noisy tags and diverse images.
Findings
The proposed algorithm outperforms baseline methods across 300 topics.
The CRF word mapping improves semantic consistency of tags.
The method effectively handles noisy, diverse image tags from large datasets.
Abstract
In this paper we introduce the problem of determining the topic that a set of images is describing, where every topic is represented as a set of words. Different from other problems like tag assignment or similar, a) we assume multiple images are used as input instead of single image, b) Input images are typically not visually related, c) Input images are not necessarily semantically close, and d) Output word space is unconstrained. In our proposed solution, visual information of each query image is used to retrieve similar images with text labels (tags) from an image database. We consider a scenario where the tags are very noisy and diverse, given that they were obtained by implicit crowd-sourcing in a database of 1 million images and over seventy seven thousand tags. The words or tags associated to each query are processed jointly in a word selection algorithm using random walks that…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Algorithms and Data Compression
