Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, i.e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. This paper proposes effective methods to learn the structure and parameters of MST-AOG. The inference based on MST-AOG enables action recognition from novel views. The training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.
This paper proposes a general benchmark for interactive segmentation algorithms. The main contribution can be summarized as follows: (I) A new dataset of fifty images is released. These images are categorized into five groups: animal, artifact, human, building and plant. They cover several major challenges for the interactive image segmentation task, including fuzzy boundary, complex texture, cluttered background, shading effect, sharp corner, and overlapping color. (II) We propose two types of schemes, pointprocess and boundary-process, to generate user scribbles automatically. The point-process simulates the human interaction process that users incrementally draw scribbles to some major components of the image. The boundaryprocess simulates the refining process that users place more scribbles near the segment boundaries to refine the details of result segments. (III) We then apply two precision measures to quantitatively evaluate the result segments of different algorithm. The region precision measures how many pixels are correctly classified, and the boundary precision measures how close is the segment boundary to the real boundary. This benchmark offered a tentative way to guarantee evaluation fairness of person-oriented tasks. Based on the benchmark, five state-of-the-art interactive segmentation algorithms are evaluated. All the images, synthesized user scribbles, running results are publicly available on the webpage 1 .
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