We propose an efficient method to train multiple object detectors simultaneously using a large scale image dataset. The one-vs-all approach that optimizes the boundary between positive samples from a target class and negative samples from the others has been the most standard approach for object detection. However, because this approach trains each object detector independently, the scores are not balanced between object classes. The proposed method combines ideas derived from both detection and classification in order to balance the scores across all object classes. We optimized the boundary between target classes and their “hard negative” samples, just as in detection, while simultaneously balancing the detector scores across object classes, as done in multi-class classification. We evaluated the performances on multi-class object detection using a subset of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2011 dataset and showed our method outperformed a de facto standard method.

Optical flow is a widely used technique for extracting flow information from video images. While it is useful for estimating temporary movement in video images, it only captures one aspect of extracting dominant flow information from a sequence of video images. In this paper, we propose a novel flow extraction approach called causal flow, which can estimate the dominant causal relationships among nearby pixels. We assume flows in video images as pixel-to-pixel information transfer, whereas the optical flow measures the relative motion of pixels. Causal flow is based on the Granger causality test, which measures causal influence based on prediction via vector autoregression, and is widely used in economics and brain science. The experimental results demonstrate that causal flow can extract dominant flow information which cannot be obtained by current methods.

Spatial Pyramid Representation (SPR) is a widely used method for embedding both global and local spatial information into a feature, and it shows good performance in terms of generic image recognition. In SPR, the image is divided into a sequence of increasingly finer grids on each pyramid level. Features are extracted from all of the grid cells and are concatenated to form one huge feature vector. As a result, expensive computational costs are required for both learning and testing. Moreover, because the strategy for partitioning the image at each pyramid level is designed by hand, there is weak theoretical evidence of the appropriate partitioning strategy for good categorization. In this paper, we propose discriminative SPR, which is a new representation that forms the image feature as a weighted sum of semi-local features over all pyramid levels. The weights are automatically selected to maximize a discriminative power. The resulting feature is compact and preserves high discriminative power, even in low dimension. Furthermore, the discriminative SPR can suggest the distinctive cells and the pyramid levels simultaneously by observing the optimal weights generated from the fine grid cells.

Optical flow is a widely used technique for extracting flow information from video images. While it is useful for estimating temporary movement in video images, it only captures one aspect of extracting dominant flow information from a sequence of video images. In this paper, we propose a novel flow extraction approach called causal flow, which can estimate the dominant causal relationships among nearby pixels. We assume flows in video images as pixel-to-pixel information transfer, whereas the optical flow measures the relative motion of pixels. Causal flow is based on Granger causality test, which measures causal influence based on prediction via vector autoregression, and is widely used in economics and brain science. The experimental results demonstrate that causal flow can extract dominant flow information which cannot be obtained by current methods.