2 research outputs found

    Fine-grained Incident Video Retrieval with Video Similarity Learning.

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    PhD ThesesIn this thesis, we address the problem of Fine-grained Incident Video Retrieval (FIVR) using video similarity learning methods. FIVR is a video retrieval task that aims to retrieve all videos that depict the same incident given a query video { related video retrieval tasks adopt either very narrow or very broad scopes, considering only nearduplicate or same event videos. To formulate the case of same incident videos, we de ne three video associations taking into account the spatio-temporal spans captured by video pairs. To cover the benchmarking needs of FIVR, we construct a large-scale dataset, called FIVR-200K, consisting of 225,960 YouTube videos from major news events crawled from Wikipedia. The dataset contains four annotation labels according to FIVR de nitions; hence, it can simulate several retrieval scenarios with the same video corpus. To address FIVR, we propose two video-level approaches leveraging features extracted from intermediate layers of Convolutional Neural Networks (CNN). The rst is an unsupervised method that relies on a modi ed Bag-of-Word scheme, which generates video representations from the aggregation of the frame descriptors based on learned visual codebooks. The second is a supervised method based on Deep Metric Learning, which learns an embedding function that maps videos in a feature space where relevant video pairs are closer than the irrelevant ones. However, videolevel approaches generate global video representations, losing all spatial and temporal relations between compared videos. Therefore, we propose a video similarity learning approach that captures ne-grained relations between videos for accurate similarity calculation. We train a CNN architecture to compute video-to-video similarity from re ned frame-to-frame similarity matrices derived from a pairwise region-level similarity function. The proposed approaches have been extensively evaluated on FIVR- 200K and other large-scale datasets, demonstrating their superiority over other video retrieval methods and highlighting the challenging aspect of the FIVR problem

    A BoF model based CBCD system using hierarchical indexing and feature similarity constraints

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