A novel object tracking algorithm based on compressed sensing and entropy of information

Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD

Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction

Peer reviewedPublisher PD

Dimension Reduction Using Samples’ Inner Structure Based Graph for Face Recognition

Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant (no. 20120061110045) and (2) the Natural Science Foundation of Jilin Province of China under Grant (no. 201115022).Peer reviewedPublisher PD

Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction

In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). Our proposed building recognition method is presented as a two-stage model: in the first stage, a building image is divided into 4 × 5 subregions, and gist vectors are extracted from these regions individually. Then, we combine these gist vectors into a matrix with relatively high dimensions. In the second stage, we proposed CCI-DR to project the high dimensional manifold matrix to low dimensional subspace. Compared with the previous building recognition method the advantages of our proposed method are that (1) gist features extracted by SM-gist have the ability to adapt to nonuniform illumination and that (2) CCI-DR can address the limitation of traditional dimensionality reduction methods, which convert gist matrices into vectors and thus mix the corresponding gist vectors from different feature maps. Our building recognition method is evaluated on the Sheffield buildings database, and experiments show that our method can achieve satisfactory performance

An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning

Peer reviewedPublisher PD

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample

Combining Deep Learning and Preference Learning for Object Tracking

International Conference on Neural Information Processing, ICONIP 2016 (23th. 2016. Kyoto, Japan)Object tracking is nowadays a hot topic in computer vision. Generally speaking, its aim is to find a target object in every frame of a video sequence. In order to build a tracking system, this paper proposes to combine two different learning frameworks: deep learning and preference learning. On the one hand, deep learning is used to automatically extract latent features for describing the multi-dimensional raw images. Previous research has shown that deep learning has been successfully applied in different computer vision applications. On the other hand, object tracking can be seen as a ranking problem, in the sense that the regions of an image can be ranked according to their level of overlapping with the target object. Preference learning is used to build the ranking function. The experimental results of our method, called DPL2DPL2(Deep & Preference Learning), are competitive with respect to the state-of-the-art algorithm

Real Environment Warning Model for Visually Impaired People in Trouble on the Blind Roads Based on Wavelet Scattering Network

When the visually impaired walk on the blind road, once they encounter obstacles to block the road, it will bring panic and even safety risks to the visually impaired. To solve this problem, based on the wavelet scattering network (WSN), this study proposes a real environment warning mode for visually impaired people when they are in trouble on the blind road. When the blind road is interrupted or obstructed, visually impaired people will experience tension or anxiety. In this study, Based on electroencephalogram (EEG) signals, this study uses the WSN method to identify the emotional state of visually impaired people, and then determine whether they need assistance. The wavelet scattering coefficients of EEG signals are extracted using the WSN method, resulting in the formation of a feature matrix. Subsequently, a support vector machine is employed for the purposes of classification and recognition. The results show that the recognition accuracy of this method reaches 95.11% in the three states of normal emotional state, general nervous state, and very nervous state of the created datasets. The classification accuracy on the SEED-IV dataset is 86.14%. The WSN method suggested in this study exhibits superior recognition accuracy and the quickest algorithm execution time when compared to previous emotion identification methods. In addition, compared with the no-warning model, the warning model proposed in this study can greatly reduce the time it takes for visually impaired people to wait for help. The WSN-based warning mode provides more reliable travel assistance for visually impaired people and reduces the risk of accidental injury