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Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
A large number of images are available on online photo-sharing services along with rich meta-data, including tags, groups, and locations, etc. For associating two domains of different modalities, e.g. images and tags, Canonical Correlation Analysis (CCA) and its extended methods are used widely. We employ a more flexible graph embedding method called Cross-Domain Matching Correlation Analysis (CDMCA),...
In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as...
We present an elegant solution to joint pre-alignment and rigid point set registration, given prior matches. Instead of performing pre-alignment and the actual registration in the separate steps, prior matches explicitly influence the registration procedure in our approach. This results in several advantages. Firstly, our approach solves the pre-alignment task — an approximate resolving of rotation...
This paper proposes an efficient design method of nonseparable oversampled lapped transform (NSOLT). NSOLT is a multidimensional redundant transform which satisfies the nonseparable, symmetric, real-valued, overlapped, compact-supported and perfect-reconstruction property. A typical example-based design approach, which consists of sparse coding and parameter optimization, is applicable to NSOLT. In...
This paper proposes a new depth image recovery algorithm which recovers a high resolution depth image using RGB color image from a very low resolution depth image. In order to achieve a high recovery performance, this paper represents the high resolution depth image as the sum of an average distance image and a surface image. Experimental examples show that the proposed algorithm achieves a high resolution...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
A discriminative dictionary learning algorithm is proposed to find sparse signal representations using relative attributes as the available semantic information. In contrast, existing (discriminative) dictionary learning (DDL) approaches mostly utilize binary label information to enhance the discriminative property of the signal reconstruction residual, the sparse coding vectors or both. Compared...
Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra- and inter-view correlations in summarizing multi-view videos. In this paper, we introduce a novel framework for summarizing multi-view videos in a way that takes into consideration both intra- and inter-view correlations in a joint...
Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Nonnegative matrix factorization (NMF) based hyperspectral unmixing aims at estimating pure spectral signatures and their fractional abundances at each pixel. During the past several years, manifold structures have been introduced as regularization constraints into NMF. However, most methods only consider the constraints on abundance matrix while ignoring the geometric relationship of endmembers....
Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data...
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same...
Anthropology studies show that genetic features are inherited by children from their parents resulting in visual resemblance between them. This paper presents a novel SIFT flow based genetic Fisher vector feature (SF-GFVF) which enhances the facial genetic features for kinship verification. The proposed SF-GFVF feature is derived by applying a novel similarity enhancement method based on SIFT flow...
In this paper, we propose a novel tracking algorithm based on joint learning hash codes and distance metric. We formulate the visual tracking as an Approximate Nearest Neighbor (ANN) searching process in which hashing methods have achieved promising performances. But most existing hashing methods rely on an affinity or similarity matrix measured by simple Euclidean distance. To obtain more robust...
In photon-limited image reconstruction, the behavior of noise at the detector end is more accurately modeled as a Poisson point process than the common choice of a Gaussian distribution. As such, to recover the original signal more accurately, a penalized negative Poisson log-likelihood function — and not a least-squares function — is minimized. In many applications, including medical imaging, additional...
We present new global convergence results for half-quadratic optimization in the context of image reconstruction. In particular, we do not assume that the inner optimization problem is solved exactly and we include the problematic cases where the objective function is nonconvex and has a continuum of stationary points. The inexact algorithm is modeled by a set-valued map defined from the majorization-minimization...
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