text: In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method outperforms subspace clustering on separate observations and the state of the art collaborative learning algorithm.
  @cite_4: A novel framework for enhancing the resolution of a low-resolution multispectral or hyperspectral image using a high resolution panchromatic image or multispectral image is proposed in this paper. This framework can be further used to perform more general types of image fusion. To create the enhanced image, a convex objective function is minimised, which preserves both the pixel affinity learnt from the high resolution image and spectral information from the low resolution image. A fast approximation method is discussed. Quantitive and qualitative analysis against existing methods shows that our method is comparable to state of the art with faster running time and greater flexibility. MATLAB code for our proposed method and the compared methods are freely available in the FuseBox package.
  @cite_33: 
  @cite_41: We describe two recently proposed randomized algorithms for the construction of low-rank approximations to matrices, and demonstrate their application (inter alia) to the evaluation of the singular value decompositions of numerically low-rank matrices. Being probabilistic, the schemes described here have a finite probability of failure; in most cases, this probability is rather negligible (10−17 is a typical value). In many situations, the new procedures are considerably more efficient and reliable than the classical (deterministic) ones; they also parallelize naturally. We present several numerical examples to illustrate the performance of the schemes.
  @cite_1: We propose a new method to quickly and accurately predict human pose---the 3D positions of body joints---from a single depth image, without depending on information from preceding frames. Our approach is strongly rooted in current object recognition strategies. By designing an intermediate representation in terms of body parts, the difficult pose estimation problem is transformed into a simpler per-pixel classification problem, for which efficient machine learning techniques exist. By using computer graphics to synthesize a very large dataset of training image pairs, one can train a classifier that estimates body part labels from test images invariant to pose, body shape, clothing, and other irrelevances. Finally, we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs in under 5ms on the Xbox 360. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state-of-the-art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.
  @cite_3: Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time.
  @cite_45: We consider the problem of building high- level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 bil- lion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a clus- ter with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental re- sults reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bod- ies. Starting with these learned features, we trained our network to obtain 15.8 accu- racy in recognizing 20,000 object categories from ImageNet, a leap of 70 relative im- provement over the previous state-of-the-art.
  @cite_34: 
summary: The motivation is two fold: Combining information encoded in multiple observations has the potential to dramatically increase the discriminative power of the coefficient matrix since we can obtain a consensus, which should be more robust to outliers and noise. We demonstrate this in our later experiments. Proliferation of sensors and reduced cost. Multi-sensor data is becoming increasingly common. For example in remote sensing, imagery is often captured from multiple sensors, each dedicated to particular frequency ranges of the electromagnetic spectrum @cite_4 @cite_33 and in human activity recognition there has been a surge in the use of RGB-Depth cameras @cite_1 @cite_3 . Moreover, single observations can be turned into multiple tertiary observations. For example many image classification and recognition algorithms use many image features is used to represent the original image @cite_41 @cite_34 @cite_45 . Each of these features is actually a new observation or appearance. See Figure for an example.