Order-aware exemplars for structuring video sets: Clustering, aligned matching and retrieval by similarity

Matsuyama, Yasuo; Shikano, Akihiro; Iwase, Hiromichi; Horie, Teruki

doi:10.1109/ijcnn.2015.7280423

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Therefore, we consider k-means methods that can identify exemplars directly. For this reason, we omit further details regarding TPKM and TSKM, however, they can be found in [14].…”

Section: Time-split K-means (Tskm)mentioning

confidence: 99%

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Similar Video Retrieval via Order-Aware Exemplars and Alignment

Horie¹,

Uchida²,

Matsuyama³

2018

JSIP

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In this paper, we present machine learning algorithms and systems for similar video retrieval. Here, the query is itself a video. For the similarity measurement, exemplars, or representative frames in each video, are extracted by unsupervised learning. For this learning, we chose the order-aware competitive learning. After obtaining a set of exemplars for each video, the similarity is computed. Because the numbers and positions of the exemplars are different in each video, we use a similarity computing method called M-distance, which generalizes existing global and local alignment methods using followers to the exemplars. To represent each frame in the video, this paper emphasizes the Frame Signature of the ISO/IEC standard so that the total system, along with its graphical user interface, becomes practical. Experiments on the detection of inserted plagiaristic scenes showed excellent precision-recall curves, with precision values very close to 1. Thus, the proposed system can work as a plagiarism detector for videos. In addition, this method can be regarded as the structuring of unstructured data via numerical labeling by exemplars. Finally, further sophistication of this labeling is discussed.

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“…Therefore, we consider k-means methods that can identify exemplars directly. For this reason, we omit further details regarding TPKM and TSKM, however, they can be found in [14].…”

Section: Time-split K-means (Tskm)mentioning

confidence: 99%

“…Next, we pay attention to an approximate k-means method called the pairwise nearest neighbor vector quantization (PNN-VQ, or simply PNN) [15] which inherits the sequential decimation of data points of [16]. We further modify the original PNN as follows so that exemplars can be obtained directly [14].…”

Section: Modified Pairwise Nearest Neighbormentioning

confidence: 99%