Asymmetric Transfer Hashing With Adaptive Bipartite Graph Learning.

Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, the known hashing methods assume that the query and retrieval samples lie in homogeneous feature space within the same domain. As a result, they cannot be directly applied to heterogeneous cross-domain retrieval. In this article, we propose a generalized image transfer retrieval (GITR) problem, which encounters two crucial bottlenecks: 1) the query and retrieval samples may come from different domains, leading to an inevitable domain distribution gap and 2) the features of the two domains may be heterogeneous or misaligned, bringing up an additional feature gap. To address the GITR problem, we propose an asymmetric transfer hashing (ATH) framework with its unsupervised/semisupervised/supervised realizations. Specifically, ATH characterizes the domain distribution gap by the discrepancy between two asymmetric hash functions, and minimizes the feature gap with the help of a novel adaptive bipartite graph constructed on cross-domain data. By jointly optimizing asymmetric hash functions and the bipartite graph, not only can knowledge transfer be achieved but information loss caused by feature alignment can also be avoided. Meanwhile, to alleviate negative transfer, the intrinsic geometrical structure of single-domain data is preserved by involving a domain affinity graph. Extensive experiments on both single-domain and cross-domain benchmarks under different GITR subtasks indicate the superiority of our ATH method in comparison with the state-of-the-art hashing methods.

Fine Tuning of Deep Contexts Toward Improved Perceptual Quality of In-Paintings.

Over the recent years, a number of deep learning approaches are successfully introduced to tackle the problem of image in-painting for achieving better perceptual effects. However, there still exist obvious hole-edge artifacts in these deep learning-based approaches, which need to be rectified before they become useful for practical applications. In this article, we propose an iteration-driven in-painting approach, which combines the deep context model with the backpropagation mechanism to fine-tune the learning-based in-painting process and hence, achieves further improvement over the existing state of the arts. Our iterative approach fine tunes the image generated by a pretrained deep context model via backpropagation using a weighted context loss. Extensive experiments on public available test sets, including the CelebA, Paris Streets, and PASCAL VOC 2012 dataset, show that our proposed method achieves better visual perceptual quality in terms of hole-edge artifacts compared with the state-of-the-art in-painting methods using various context models.

Robust soft-decision interpolation using weighted least squares.

Soft-decision adaptive interpolation (SAI) provides a powerful framework for image interpolation. The robustness of SAI can be further improved by using weighted least-squares estimation, instead of least-squares estimation in both of the parameter estimation and data estimation steps. To address the mismatch issue of "geometric duality" during parameter estimation, the residuals (prediction errors) are weighted according to the geometric similarity between the pixel of interest and the residuals. The robustness of data estimation can be improved by modeling the weights of residuals with the well-known bilateral filter. Experimental results show that there is a 0.25-dB increase in peak signal-to-noise ratio (PSNR) for a sample set of natural images after the suggested improvements are incorporated into the original SAI. The proposed algorithm produces the highest quality in terms of PSNR and subjective quality among sophisticated algorithms in the literature.