http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A Novel Extreme Learning Machine based Denoising Algorithm
Zhiyong Fan,Quansen Sun,Feng Ruan,Kai Hu,Jin Wang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.2
We introduce a fast and effective algorithm extreme learning machine (ELM) and apply it to image denoising. GA-ELM algorithm we proposed uses genetic algorithm(GA) to decide weights and bias in the ELM. It has better global optimal characteristics than traditional optimal ELM algorithm. In this paper, we used GA-ELM to do image denosing researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utilize the well-trained GA-ELM to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-ELM. Experiment data shows that this algorithm has better performance than other denosing algorithm.
Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification
Xu, Mengxi,Sun, Quansen,Lu, Yingshu,Shen, Chenming The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.
Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification
Mengxi Xu,Quansen Sun,Yingshu Lu,Chenming Shen 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.
Medical Image Retrieval with Relevance Feedback via Pairwise Constraint Propagation
( Menglin Wu ),( Qiang Chen ),( Quansen Sun ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.1
Relevance feedback is an effective tool to bridge the gap between superficial image contents and medically-relevant sense in content-based medical image retrieval. In this paper, we propose an interactive medical image search framework based on pairwise constraint propagation. The basic idea is to obtain pairwise constraints from user feedback and propagate them to the entire image set to reconstruct the similarity matrix, and then rank medical images on this new manifold. In contrast to most of the algorithms that only concern manifold structure, the proposed method integrates pairwise constraint information in a feedback procedure and resolves the small sample size and the asymmetrical training typically in relevance feedback. We also introduce a long-term feedback strategy for our retrieval tasks. Experiments on two medical image datasets indicate the proposed approach can significantly improve the performance of medical image retrieval. The experiments also indicate that the proposed approach outperforms previous relevance feedback models.
Progressive multi-atlas label fusion by dictionary evolution
Song, Yantao,Wu, Guorong,Bahrami, Khosro,Sun, Quansen,Shen, Dinggang Elsevier 2017 Medical image analysis Vol.36 No.-
<P><B>Abstract</B></P> <P>Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an <I>atlas patch dictionary</I> (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the <I>atlas label dictionary</I> (in the label domain). However, due to the generally large gap between the patch appearance in the <I>image domain</I> and the patch structure in the <I>label domain</I>, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer <I>static</I> dictionary to the multi-layer <I>dynamic</I> dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer <I>static</I> dictionary.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A progressive multi-atlas label fusion method by deep dictionary evolution is proposed. </LI> <LI> A sequence of intermediate dictionaries was constructed to progressively optimize the weights for label fusion. </LI> <LI> As an extension of the conventional single-layer methods by improving their label fusion performance. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>