In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos. Since the frame-wise feature mean is unable to characterize the pose diversity among frames, we define and preserve the overall pose diversity and closeness in a video. Then, identity will be the only source of variation across videos since the pose varies even within a single video. Instead of simply using all the frames, we select those faces whose pose point is closest to the centroid of the K-means cluster containing that pose point. Then, we represent a video as a bag of frame-wise deep face features while the number of features has been reduced from hundreds to K. Since the video representation can well represent the identity, now we measure the subject similarity between two videos as the max correlation among all possible pairs in the two bags of features. On the official 5,000 video-pairs of the YouTube Face dataset for face verification, our algorithm achieves a comparable performance with VGG-face that averages over deep features of all frames. Other vision tasks can also benefit from the generic idea of employing geometric cues to improve the descriptiveness of deep features.
The cosine similarity or correlation both are well-defined metrics for measuring the similarity of two images. A simple adaptation to videos will be randomly sampling a frame from each of the video. However, the correlation between two random image samples might characterize cues other than identity (say, the pose similarity). There are existing works on measuring the similarity of two videos using manifold-to-manifold distance @cite_3 . However, the straightforward extension of image-based correlation is preferred for its simplicity, such as temporal max or mean pooling @cite_0 . The impact of different spatial pooling methods in CNN such as mean pooling, max pooling and @math -2 pooling, has been discussed in the literature @cite_10 @cite_12 . However, pooling over the time domain is not as straightforward as spatial pooling. The frame-wise feature mean is a straightforward video-level representation and yet not a robust statistic. Despite that, temporal mean pooling is conventional to represent a video such as average pooling for video-level representation @cite_15 , mean encoding for face recognition @cite_8 , feature averaging for action recognition @cite_1 and mean pooling for video captioning @cite_7 .
@cite_7 @cite_8 @cite_1 @cite_3 @cite_0 @cite_15 @cite_10 @cite_12
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In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Given the video sequence of a person, features from each frame that are extracted from all levels of a deep convolutional network can preserve a higher spatial resolution from which we can model finer motion patterns. These low-level visual percepts are leveraged into a variant of recurrent model to characterize the temporal variation between time-steps. Features from all time-steps are then summarized using temporal pooling to produce an overall feature representation for the complete sequence. The deep convolutional network, recurrent layer, and the temporal pooling are jointly trained to extract comparable hidden-unit representations from input pair of time series to compute their corresponding similarity value. The proposed framework combines time series modeling and metric learning to jointly learn relevant features and a good similarity measure between time sequences of person. Experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose. We present a novel action recognition method based on space-time locally adaptive regression kernels and the matrix cosine similarity measure. The proposed method uses a single example of an action as a query to find similar matches. It does not require prior knowledge about actions, foreground background segmentation, or any motion estimation or tracking. Our method is based on the computation of novel space-time descriptors from the query video which measure the likeness of a voxel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target video. This comparison is done using a matrix generalization of the cosine similarity measure. The algorithm yields a scalar resemblance volume, with each voxel indicating the likelihood of similarity between the query video and all cubes in the target video. Using nonparametric significance tests by controlling the false discovery rate, we detect the presence and location of actions similar to the query video. High performance is demonstrated on challenging sets of action data containing fast motions, varied contexts, and complicated background. Further experiments on the Weizmann and KTH data sets demonstrate state-of-the-art performance in action categorization. We introduce a behavior-based similarity measure which tells us whether two different space-time intensity patterns of two different video segments could have resulted from a similar underlying motion field. This is done directly from the intensity information, without explicitly computing the underlying motions. Such a measure allows us to detect similarity between video segments of differently dressed people performing the same type of activity. It requires no foreground background segmentation, no prior learning of activities, and no motion estimation or tracking. Using this behavior-based similarity measure, we extend the notion of 2-dimensional image correlation into the 3-dimensional space-time volume, thus allowing to correlate dynamic behaviors and actions. Small space-time video segments (small video clips) are "correlated" against entire video sequences in all three dimensions (x,y, and t). Peak correlation values correspond to video locations with similar dynamic behaviors. Our approach can detect very complex behaviors in video sequences (e.g., ballet movements, pool dives, running water), even when multiple complex activities occur simultaneously within the field-of-view of the camera. We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher-dimensional transform-domain representation is leveraged to enforce sparsity and thus regularize the data. The proposed algorithm exploits the mutual similarity between 3-D spatiotemporal volumes constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e. self-similarity) along the fourth dimension. Collaborative filtering is realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original position in the video. Experimental results demonstrate the effectiveness of the proposed procedure which outperforms the state of the art.