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Luo J.,Beihang University | Jiang Z.,Beijing Key Laboratory of Digital Media
Proceedings - International Conference on Pattern Recognition | Year: 2014

This paper addresses the problem of learning semantic compact binary codes for efficient retrieval in large-scale image collections. Our contributions are three-fold. Firstly, we introduce semantic codes, of which each bit corresponds to an attribute that describes a property of an object (e.g. dogs have furry). Secondly, we propose to use matrix factorization (MF) to learn the semantic codes by encoding attributes. Unlike traditional PCA-based encoding methods which quantize data into orthogonal bases, MF assumes no constraints on bases, and this scheme is coincided with that attributes are correlated. Finally, to augment semantic codes, MF is extended to encode extra non-semantic codes to preserve similarity in origin data space. Evaluations on a-Pascal dataset show that our method is comparable to the state-of-the-art when using Euclidean distance as ground truth, and even outperforms state-of-the-art when using class label as ground truth. Furthermore, in experiments, our method can retrieve images that share the same semantic properties with the query image, which can be used to other vision tasks, e.g. re-training classifiers. © 2014 IEEE.


Zhang H.,Beihang University | Zhang H.,Beijing Key Laboratory of Digital Media | Jiang Z.,Beihang University | Jiang Z.,Beijing Key Laboratory of Digital Media
Chinese Journal of Aeronautics | Year: 2014

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions. © 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.


Wu J.,Beihang University | Jiang Z.,Beihang University | Yang J.,Beijing Key Laboratory of Digital Media | Luo J.,Beijing Key Laboratory of Digital Media
Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013 | Year: 2013

The identification of shadow and shading boundaries is a key step towards reducing the imaging effects that are caused by direct illumination of the light source in the scene. Discriminating shadow boundaries from images of natural scenes has been widely applied in the field of computer vision such as object recognition, intelligent monitoring and image understanding. In this paper, we propose a method to identify shadow boundaries based on multiple kernel learning. We first extract all possible candidate boundaries and then analyze their properties. Unlike the previous proposed methods which simply combine features as a vector, we choose the optimal kernel function for every feature and learn the correct weights of different features from training database. At last, we link shadow boundaries fragments together to get longer and complete shadow boundaries. The experiment results show that the method we propose works well in shadow boundaries identification. © 2013 IEEE.


Zhang H.,Beihang University | Zhang H.,Beijing Key Laboratory of Digital Media | Jiang Z.,Beihang University | Jiang Z.,Beijing Key Laboratory of Digital Media | Elgammal A.,Rutgers University
Acta Astronautica | Year: 2013

Imaging sensors are widely used in aerospace recently. In this paper, a vision-based approach for estimating the pose of cooperative space objects is proposed. We learn generative model for each space object based on homeomorphic manifold analysis. Conceptual manifold is used to represent pose variation of captured images of the object in visual space, and nonlinear functions mapping between conceptual manifold representation and visual inputs are learned. Given such learned model, we estimate the pose of a new image by minimizing a reconstruction error via a traversal procedure along the conceptual manifold. Experimental results on the simulated image dataset show that our approach is effective for 1D and 2D pose estimation. © 2013 IAA.


Zhang H.,Beihang University | Zhang H.,Beijing Key Laboratory of Digital Media | Jiang Z.,Beihang University | Jiang Z.,Beijing Key Laboratory of Digital Media
Journal of Computational Information Systems | Year: 2014

Images of the same object lie on a low-dimensional manifold (view manifold) in the visual space. View manifolds can be used to represent viewpoint variation of multi-view images in the embedding space, and can be very helpful to multi-view object detection, classification, and viewpoint estimation. In this paper, we introduce a conceptual manifold as a common representation of all view manifolds. In order to evaluate the performance of the conceptual manifold representation, we learn a generative model that can map from the manifold representation to visual inputs for the tasks of arbitrary view image synthesis and viewpoint estimation. We did experiments on COIL-20 dataset, and compared with popular manifold learning methods. Experimental results show that our conceptual manifold representation can effectively describe the viewpoint variation of multi-view images with strong robustness, and outperform the view manifolds learned by popular manifold learning methods. © 2014 Binary Information Press.

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