2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献_第3页

Image retrieval based on saliency for urban image contents 基于显著性的城市图像内容检索

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310131

Kamel Guissous, V. Gouet-Brunet

{"title":"Image retrieval based on saliency for urban image contents","authors":"Kamel Guissous, V. Gouet-Brunet","doi":"10.1109/IPTA.2017.8310131","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310131","url":null,"abstract":"With the increase of image datasets size and of descriptors complexity in Content-Based Image Retrieval (CBIR) and Computer Vision, it is essential to find a way to limit the amount of manipulated data, while keeping its quality. Instead of treating the entire image, the selection of regions which hold the essence of information is a relevant option to reach this goal. As the visual saliency aims at highlighting the areas of the image which are the most important for a given task, in this paper we propose to exploit visual saliency maps to prune the most salient image features. A novel visual saliency approach based on the local distribution analysis of the edges orientation, particularly dedicated to structured contents, such as street view images of urban environments, is proposed. It is evaluated for CBIR according to three criteria: quality of retrieval, volume of manipulated features and computation time. The proposal can be exploited into various applications involving large sets of local visual features; here it is experimented within two applications: cross-domain image retrieval and image-based vehicle localisation.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127542734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Weighted hybrid features for person re-identification 基于加权混合特征的人再识别

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310107

Saba Mumtaz, Naima Mubariz, S. Saleem, M. Fraz

引用次数: 17

Detecting exercise-induced fatigue using thermal imaging and deep learning 利用热成像和深度学习检测运动引起的疲劳

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310151

Miguel Bordallo López, Carlos R. del-Blanco, N. García

引用次数: 24

Dynamic ensemble selection VS K-NN: Why and when dynamic selection obtains higher classification performance? 动态集成选择VS K-NN:动态选择为何以及何时获得更高的分类性能?

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310100

Rafael M. O. Cruz, Hiba H. Zakane, R. Sabourin, George D. C. Cavalcanti

{"title":"Dynamic ensemble selection VS K-NN: Why and when dynamic selection obtains higher classification performance?","authors":"Rafael M. O. Cruz, Hiba H. Zakane, R. Sabourin, George D. C. Cavalcanti","doi":"10.1109/IPTA.2017.8310100","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310100","url":null,"abstract":"Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising MCS approaches is Dynamic Selection (DS), which relies on finding the most competent classifier or ensemble of classifiers to predict each test sample. The majority of the DS techniques are based on the K-Nearest Neighbors (K-NN) definition, and the quality of the neighborhood has a huge impact on the performance of DS methods. In this paper, we perform an analysis comparing the classification results of DS techniques and the K-NN classifier under different conditions. Experiments are performed on 18 state-of-the-art DS techniques over 30 classification datasets and results show that DS methods present a significant boost in classification accuracy even though they use the same neighborhood as the K-NN. The reasons behind the outperformance of DS techniques over the K-NN classifier reside in the fact that DS techniques can deal with samples with a high degree of instance hardness (samples that are located close to the decision border) as opposed to the K-NN. In this paper, not only we explain why DS techniques achieve higher classification performance than the K-NN but also when DS should be used.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Deriving high-level scene descriptions from deep scene CNN features 从深度场景CNN特征中提取高级场景描述

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310111

Akram Bayat, M. Pomplun

{"title":"Deriving high-level scene descriptions from deep scene CNN features","authors":"Akram Bayat, M. Pomplun","doi":"10.1109/IPTA.2017.8310111","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310111","url":null,"abstract":"In this paper, we generate two computational models in order to estimate two dominant global properties (naturalness and openness) for representing a scene based on its global spatial structure. Naturalness and openness are two dominant perceptual properties within a multidimensional space in which semantically similar scenes (e.g., corridor and hallway) are assigned to nearby points. In this model space, the representation of a real-world scene is based on the overall shape of a scene but not on local object information. We introduce the use of a deep convolutional neural network for generating features that are well-suited for estimating the two global properties of a visual scene. The extracted features are integrated in an efficient way and fed into a linear support vector machine (SVM) to classify naturalness versus man-madeness and openness versus closedness. These two global properties (naturalness and openness) of an input image can be predicted from activations in the lowest layer of the convolutional neural network which has been trained for a scene recognition task. The consistent results of computational models in full and restricted spatial frequency ranges suggest that the representation of an image in the lowest layer of the deep scene CNN contains holistic information of the images as it leads to highest accuracy in modelling the global shape of the scene.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Multi-modal data fusion for pain intensity assessment and classification 多模态数据融合用于疼痛强度评估和分类

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310115

Patrick Thiam, F. Schwenker

引用次数: 21

Coarse-to-fine texture analysis for inner cell mass identification in human blastocyst microscopic images 人囊胚显微图像内细胞团鉴别的粗-细纹理分析

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310152

Reza Moradi Rad, Parvaneh Saeedi, J. Au, J. Havelock

引用次数: 9

Comparing keyframe summaries of egocentric videos: Closest-to-centroid baseline 比较自我中心视频的关键帧摘要:最接近质心基线

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310123

L. Kuncheva, Paria Yousefi, J. Almeida

引用次数: 2

Recursive 3D scene estimation with multiple camera pairs 多相机对的三维场景递归估计

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310129

Torsten Engler, Hans-Joachim Wünsche

{"title":"Recursive 3D scene estimation with multiple camera pairs","authors":"Torsten Engler, Hans-Joachim Wünsche","doi":"10.1109/IPTA.2017.8310129","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310129","url":null,"abstract":"In this paper we present the recursive estimation of static scenes with multiple stereo camera pairs. The estimation is based on a point cloud created from the disparities of the cameras. The focus lies on reducing erroneous measurements while obtaining a comparatively dense measurement in real time. While recursive scene estimation via stereo cameras has been presented several times before, the estimation has never been exploited in the measurement algorithm. We propose the usage of the current scene estimation in the disparity measurement to increase robustness, denseness and outlier rejection. A scene prior is created for each measurement using OpenGL taking occlusions, camera positions and existence probability into account. Additionally, multiple stereo pairs with different alignment provide distinct information. Each disparity measurement benefits from the complete scene knowledge the other stereo camera pairs provide. The creation of new points for the point cloud is based on a scaled version of the current scene and allows for simple trade-off between computational effort and point cloud denseness.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114717798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

EEG source imaging based on spatial and temporal graph structures 基于时空图结构的脑电源成像

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310089

Jing Qin, Feng Liu, Shouyi Wang, J. Rosenberger

引用次数: 8