Tianyang Ma

Maximum weight cliques with mutex constraints for video object segmentation

In this paper, we address the problem of video object segmentation, which is to automatically identify the primary object and segment the object out in every frame. We propose a novel formulation of selecting object region candidates simultaneously in all frames as finding a maximum weight clique in a weighted region graph. The selected regions are expected to have high objectness score (unary potential) as well as share similar appearance (binary potential). Since both unary and binary potentials are unreliable, we introduce two types of mutex (mutual exclusion) constraints on regions in the same clique: intra-frame and inter-frame constraints. Both types of constraints are expressed in a single quadratic form. We propose a novel algorithm to compute the maximal weight cliques that satisfy the constraints. We apply our method to challenging benchmark videos and obtain very competitive results that outperform state-of-the-art methods.

From partial shape matching through local deformation to robust global shape similarity for object detection

In this paper, we propose a novel framework for contour based object detection. Compared to previous work, our contribution is three-fold. 1) A novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. 2) Grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. 3) A novel local affine-transformation to utilize the holistic shape information for scoring and ranking the shape similarity hypotheses. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. Very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background.

Fan Shape Model for object detection

We propose a novel shape model for object detection called Fan Shape Model (FSM). We model contour sample points as rays of final length emanating for a reference point. As in folding fan, its slats, which we call rays, are very flexible. This flexibility allows FSM to tolerate large shape variance. However, the order and the adjacency relation of the slats stay invariant during fan deformation, since the slats are connected with a thin fabric. In analogy, we enforce the order and adjacency relation of the rays to stay invariant during the deformation. Therefore, FSM preserves discriminative power while allowing for a substantial shape deformation. FSM allows also for precise scale estimation during object detection. Thus, there is not need to scale the shape model or image in order to perform object detection. Another advantage of FSM is the fact that it can be applied directly to edge images, since it does not require any linking of edge pixels to edge fragments (contours).

Graph Transduction Learning with Connectivity Constraints with Application to Multiple Foreground Cosegmentation

The proposed approach is based on standard graph transduction, semi-supervised learning (SSL) framework. Its key novelty is the integration of global connectivity constraints into this framework. Although connectivity leads to higher order constraints and their number is an exponential, finding the most violated connectivity constraint can be done efficiently in polynomial time. Moreover, each such constraint can be represented as a linear inequality. Based on this fact, we design a cutting-plane algorithm to solve the integrated problem. It iterates between solving a convex quadratic problem of label propagation with linear inequality constraints, and finding the most violated constraint. We demonstrate the benefits of the proposed approach on a realistic and very challenging problem of co segmentation of multiple foreground objects in photo collections in which the foreground objects are not present in all photos. The obtained results not only demonstrate performance boost induced by the connectivity constraints, but also show a significant improvement over the state-of-the-art methods.

Boosting chamfer matching by learning chamfer distance normalization

We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.

Learning Non-linear Calibration for Score Fusion with Applications to Image and Video Classification

Image and video classification is a challenging task, particularly for complex real-world data. Recent work indicates that using multiple features can improve classification significantly, and that score fusion is effective. In this work, we propose a robust score fusion approach which learns non-linear score calibrations for multiple base classifier scores. Through calibration, original base classifiers scores are adjusted to reflect their true intrinsic accuracy and confidence, relative to the other base classifiers, in such a way that calibrated scores can be simply added to yield accurate fusion results. Our approach provides a unified approach to jointly solve score normalization and fusion classifier learning. The learning problem is solved within a max-margin framework to globally optimize performance metric on the training set. Experiments demonstrate the strength and robustness of the proposed method.

Stable Feature Selection with Minimal Independent Dominating Sets

In this paper, we focus on stable selection of relevant features. The main contribution is a novel framework for selecting most informative features which can preserve the linear combination property of the original feature space. We propose a novel formulation of this problem as selection of a minimal independent dominating set (MIDS). MIDS of a feature graph is a smallest subset such that no two of its nodes are connected and all other nodes are connected to at least one node in it. In this way, the diversity and coverage of the original feature space can be preserved. Furthermore, the proposed MIDS framework complements standard feature selection algorithms like SVM-RFE, stability lasso and ensemble SVM RFE. When these algorithms are applied to feature subsets selected by MIDS as opposed to all the input features, they select more stable features and achieve better prediction accuracy, as our experimental results clearly demonstrate.

Binarization of Gray-Level Images Based on Skeleton Region Growing

In this chapter, we introduce a new binarization method of gray level images. We first extract skeleton curves from Canny edge image. Second, a Skeleton Strength Map (SSM) is calculated from Euclidean distance transform. Starting from the boundary edges, the distance transform is firstly computed and its gradient vector field is calculated. After that, the isotropic diffusion is performed on the gradient vector field and the SSM is computed from the diffused vector field. It has two advantages that make it useful for skeletonization: 1) the SSM serves as the form of the likelihood of a pixel being a skeleton point: the value at pixels of the skeleton is large while at pixels that are away from the center of object, the SSM value decays very fast; 2) By computing the SSM from the distance transform, a parameterized noise smoothing is obtained. Then, skeleton curves are classified into foreground and background classes by comparing the mean value of their local edge pixels and neighbors lowest intensity. Finally, the binarization result is obtained by choosing foreground skeleton curve pixels as seed points and presenting region growing algorithm on gray scale image with certain growing criteria. Images with different types of document components and degradations are used to test the effectiveness of the proposed algorithm. Results demonstrate that the method performs well on images with low contrast, noise and non-uniform illumination.