Large-Scale Video Retrieval Using Image Queries

Retrieving videos from large repositories using image queries is important for many applications, such as brand monitoring or content linking. We introduce a new retrieval architecture, in which the image query can be compared directly with database videos-significantly improving retrieval scalabili...

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Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 28; číslo 6; s. 1406 - 1420
Hlavní autoři:	Araujo, Andre, Girod, Bernd
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Bloom filter Clutter Datasets Electronic mail fisher vector Image segmentation Indexes large-scale Queries query-by-image Repositories Retrieval Search problems video retrieval Visual databases Visualization
ISSN:	1051-8215, 1558-2205
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Abstract	Retrieving videos from large repositories using image queries is important for many applications, such as brand monitoring or content linking. We introduce a new retrieval architecture, in which the image query can be compared directly with database videos-significantly improving retrieval scalability compared with a baseline system that searches the database on a video frame level. Matching an image to a video is an inherently asymmetric problem. We propose an asymmetric comparison technique for Fisher vectors and systematically explore query or database items with varying amounts of clutter, showing the benefits of the proposed technique. We then propose novel video descriptors that can be compared directly with image descriptors. We start by constructing Fisher vectors for video segments, by exploring different aggregation techniques. For a database of lecture videos, such methods obtain a two orders of magnitude compression gain with respect to a frame-based scheme, with no loss in retrieval accuracy. Then, we consider the design of video descriptors, which combine Fisher embedding with hashing techniques, in a flexible framework based on Bloom filters. Large-scale experiments using three datasets show that this technique enables faster and more memory-efficient retrieval, compared with a frame-based method, with similar accuracy. The proposed techniques are further compared against pre-trained convolutional neural network features, outperforming them on three datasets by a substantial margin.
AbstractList	Retrieving videos from large repositories using image queries is important for many applications, such as brand monitoring or content linking. We introduce a new retrieval architecture, in which the image query can be compared directly with database videos-significantly improving retrieval scalability compared with a baseline system that searches the database on a video frame level. Matching an image to a video is an inherently asymmetric problem. We propose an asymmetric comparison technique for Fisher vectors and systematically explore query or database items with varying amounts of clutter, showing the benefits of the proposed technique. We then propose novel video descriptors that can be compared directly with image descriptors. We start by constructing Fisher vectors for video segments, by exploring different aggregation techniques. For a database of lecture videos, such methods obtain a two orders of magnitude compression gain with respect to a frame-based scheme, with no loss in retrieval accuracy. Then, we consider the design of video descriptors, which combine Fisher embedding with hashing techniques, in a flexible framework based on Bloom filters. Large-scale experiments using three datasets show that this technique enables faster and more memory-efficient retrieval, compared with a frame-based method, with similar accuracy. The proposed techniques are further compared against pre-trained convolutional neural network features, outperforming them on three datasets by a substantial margin.
Author	Araujo, Andre Girod, Bernd
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SubjectTerms	Artificial neural networks Bloom filter Clutter Datasets Electronic mail fisher vector Image segmentation Indexes large-scale Queries query-by-image Repositories Retrieval Search problems video retrieval Visual databases Visualization
Title	Large-Scale Video Retrieval Using Image Queries
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