NDLI: Score Propagation Based on Similarity Shot Graph for Improving Visual Object Retrieval

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Score Propagation Based on Similarity Shot Graph for Improving Visual Object Retrieval

Score Propagation Based on Similarity Shot Graph for Improving Visual Object Retrieval

Content Provider	ACM Digital Library
Author	Saavedra, Jose Manuel Barrios, Juan Manuel
Abstract	The Visual Object Retrieval problem consists in locating the occurrences of a specific entity in an image/video dataset. In this work, we focus on discovering new occurrences of an entity by propagating the detection scores of already computed candidates to other video segments. The score propagation follows the edges of a pre-computed Similarity Shot Graph (SSG). The SSG connects video segments that are similar according to some criterion. Four methods for creating the SSG are presented: two based on computing and comparing low-level visual features, one based on comparing text transcriptions, and other based on computing and comparing high-level concepts. The score propagation is evaluated on the INS 2014 dataset. The results show that the detection performance can be slightly improved by the proposed algorithm. However, the performance is variable and depends on the properties of the SSG and the target entity. It is part of the future work to automatically decide the kind of SSG that will be used to propagate scores given a set of detection candidates.
Starting Page	19
Ending Page	22
Page Count	4
File Format	PDF
ISBN	9781450337496
DOI	10.1145/2802558.2814644
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visual object retrieval Content-based search Multimedia information retrieval Similarity graph
Content Type	Text
Resource Type	Article

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