Ensemble of different approaches for a reliable person re-identification system

Nanni, Loris; Munaro, Matteo; Ghidoni, Stefano; Menegatti, Emanuele; Brahnam, Sheryl

doi:10.1016/j.aci.2015.02.002

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…For the RPE dataset only (Section 2.2) we used an additional set of descriptors, not based on texture, but on colors (COLORS). COLORS consists in the concatenation of statistics computed on the three channels of a color image: mean, homogeneity, standard deviation, third, fourth and fifth moments and the marginal histograms (8 bins for each channel) [ 48 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

et al. 2016

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AimsA fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification.MethodsFor image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level.Dataset and ResultsThe RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%.ConclusionsHere we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f.

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Section: Methodsmentioning

confidence: 99%

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

et al. 2016

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“…Video analytics is an active area of research from the recent past. Researchers have exploited the use of color and texture to perform object classification . proposed an ensemble of different approaches, and combined them on color spaces.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐based video analytics using convolutional neural networks

et al. 2018

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Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud-based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in-memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general-purpose video analytics system.

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“…Depth is estimated by means of an active triangulation process 14 between an infrared pattern projector and an infrared camera, i.e., the position of each 3D point is the intersection of the optical rays corresponding to a dot of the projector and the one of the considered pixel in the infrared camera. The Kinect is widely used in computer vision and robotics for Simultaneous Localization and Mapping (SLAM), 15 people detection and tracking, 3,13 short-term and long-term people re-identification, [16][17][18] ambient assisted living [19][20][21] and many other applications. Besides the wide usage of this sensor, it has the drawback of not being able to estimate depth information outdoors because the infrared component of the sunlight interfers with the pattern projected by Kinect.…”

Section: Rgb-d Sensorsmentioning

confidence: 99%

Cost-efficient RGB-D smart camera for people detection and tracking

Carraro

Munaro

Menegatti

2016

J. Electron. Imaging

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We describe a software library we developed for efficiently using the Kinect v2, a time-of-flight RGB-D sensor, with an embedded system, the NVidia Jetson TK1, as a cost-efficient RGB-D smart camera for people detection and tracking. The speed-up needed for achieving real-time operation has been obtained using NVidia CUDA to concurrently generate and process the raw depth and infrared data and to create the three-dimensional point cloud. This library has been released as open source and the smart camera has been tested in real-world scenarios, as a people-detection node in an open-source multinode RGB-D tracking system (OpenPTrack) and onboard a service robot for endowing it with robust people-following capabilities. Moreover, we show that nonembedded computers also can benefit from our library in terms of people-detection frame rate

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Ensemble of different approaches for a reliable person re-identification system

Cited by 16 publications

References 24 publications

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

Cloud‐based video analytics using convolutional neural networks

Cost-efficient RGB-D smart camera for people detection and tracking

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