arXiv subjects

Yixiong Liang

Publications and source records attributed to Yixiong Liang.

22 records · Page 2Linked to original sources

arXiv2018

CNN-Based Automatic Urinary Particles Recognition

The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper, we exploit CNN to learn features in an end-to-end manner to recognize the urine particles. We treat the urine particles recognition as object detection and exploit two state-of-the-art CNN-based object detection methods, Faster R-CNN and SSD, as well as their variants for urine particles recognition. We further investigate different factors involving these CNN-based object detection methods for urine particles recognition. We comprehensively evaluate these methods on a dataset consisting of 5,376 annotated images corresponding to 7 categories of urine particles, i.e., erythrocyte, leukocyte, epithelial cell, crystal, cast, mycete, epithelial nuclei, and obtain a best mAP (mean average precision) of 84.1% while taking only 72 ms per image on a NVIDIA Titan X GPU.

cs.CV↗

arXiv2011

Feature selection via simultaneous sparse approximation for person specific face verification

There is an increasing use of some imperceivable and redundant local features for face recognition. While only a relatively small fraction of them is relevant to the final recognition task, the feature selection is a crucial and necessary step to select the most discriminant ones to obtain a compact face representation. In this paper, we investigate the sparsity-enforced regularization-based feature selection methods and propose a multi-task feature selection method for building person specific models for face verification. We assume that the person specific models share a common subset of features and novelly reformulated the common subset selection problem as a simultaneous sparse approximation problem. To the best of our knowledge, it is the first time to apply the sparsity-enforced regularization methods for person specific face verification. The effectiveness of the proposed methods is verified with the challenging LFW face databases.

cs.CV↗

arXiv2011

Multi-task GLOH feature selection for human age estimation

In this paper, we propose a novel age estimation method based on GLOH feature descriptor and multi-task learning (MTL). The GLOH feature descriptor, one of the state-of-the-art feature descriptor, is used to capture the age-related local and spatial information of face image. As the exacted GLOH features are often redundant, MTL is designed to select the most informative feature bins for age estimation problem, while the corresponding weights are determined by ridge regression. This approach largely reduces the dimensions of feature, which can not only improve performance but also decrease the computational burden. Experiments on the public available FG-NET database show that the proposed method can achieve comparable performance over previous approaches while using much fewer features.

cs.CV↗

arXiv2011

Feature Selection via Sparse Approximation for Face Recognition

Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades. Accordingly, feature selection has become more and more important and plays a critical role for face data description and recognition. In this paper, we propose a trainable feature selection algorithm based on the regularized frame for face recognition. By enforcing a sparsity penalty term on the minimum squared error (MSE) criterion, we cast the feature selection problem into a combinatorial sparse approximation problem, which can be solved by greedy methods or convex relaxation methods. Moreover, based on the same frame, we propose a sparse Ho-Kashyap (HK) procedure to obtain simultaneously the optimal sparse solution and the corresponding margin vector of the MSE criterion. The proposed methods are used for selecting the most informative Gabor features of face images for recognition and the experimental results on benchmark face databases demonstrate the effectiveness of the proposed methods.

cs.CV↗