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MirBot: A collaborative object recognition system for smartphones using convolutional neural networks

NEUROCOMPUTING 2018年 293卷 87-99页

作者： Pertusa, Antonio Gallego, Antonio-Javier Bernabeu, Marisa Univ Alicante Dept Lenguajes & Sistemas Informat E-03690 Alicante Spain

MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Object recognition image datasets Convolutional neural networks Transfer learning Multimodality Human computer interaction

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Arabica coffee leaf images dataset for coffee leaf disease detection and classification

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DATA IN BRIEF 2021年 36卷 107142页

作者： Jepkoech, Jennifer Mugo, David Muchangi Kenduiywo, Benson K. Too, Edna Chebet Univ Embu POB 6 Embu 60100 Kenya Jomo Kenyatta Univ Sci & Technol POB 62000-00200 Nairobi Kenya Chuka Univ POB 109-60400 Chuka Kenya

This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under realworld conditions using a digital camera and with the help of a pathologist. JMuBEN dataset contains three compressed folders with images inside. The first file contains 7682 images of Cerscospora, the second contains 8337 images of rust and the last one contains 6572 images of Phoma. JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://***. com/datasets/tgv3zb82nd/1 and https://***/ datasets/t2r6rszp5c/1 respectively. (C) 2021 The Author(s). Published by Elsevier Inc.

关键词： Arabica coffee image datasets Machine learning Deep learning Disease diagnosis

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Building an image Database for Studying image Retargeting 14

Building an Image Database for Studying Image Retargeting

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14th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Alsmirat, Mohammad A. Qawasmeh, Ethar Al-Ayyoub, Mahmoud Damer, Nour Alhuda Jararweh, Yaser Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan

ISBN: (纸本)9781538635810

Modern electronic devices(such as TVs, laptops, and mobile devices) come with a huge variety in screen sizes, resolutions, and aspect ratios. image retargeting is a technique to retarget or (resize) an image to better utilize the viewing device screen and to protect the main content of the image. Different retargeting techniques have been proposed in the literature that mainly utilizes one of the following main techniques: cropping, seam carving, and scale and stretch. The current problem of image retargeting is that it is very hard to determine the best technique to use on an image to get a target dimension. To apply techniques such as machine learning to determine the best technique to perform image retargeting, an annotated image set is needed to perform the training step. In this work, we build and annotate an image set that is suitable to develop such advance retargeting techniques. We build a dataset that include 500 original images. We apply 4 different retargeting techniques to get two different sizes. The resulting image set contains 4000 images annotated by three people. We also analyze the annotation results to get useful remarks from the annotators perceptual point of view.

关键词： image Retargeting image datasets QoE Human Perceptual Views

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Histogram specification with generalised extreme value distribution to enhance retinal images

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ELECTRONICS LETTERS 2016年第8期52卷 596-597页

作者： Intajag, S. Kansomkeat, S. Bhurayanontachai, P. Prince Songkla Univ Fac Sci Dept Comp Sci Hat Yai Songkhla Thailand Prince Songkla Univ Fac Med Dept Ophthalmol Hat Yai Songkhla Thailand

Histogram specification usually deals with greyscale image enhancements, although, it can apply to colour images;however, generally manipulating only the luminance channel. A method for automatic enhancement of digital colour retina images by specifying all colour bands of the histograms is designed to adjust brightness, contrast, and colour balance for visual diagnosis of age-related macular degeneration. The proposed algorithm employs a generalised extreme value distribution consisting of three parameters to adjust brightness and colour balance by the location parameter, contrast and tone tuned by the scale, and shape parameters, respectively. The algorithm performance is evaluated with two image datasets: structured analysis of the retina and automated retinal image analysis. The improved results show that the specified histograms based on the generalised extreme value have been provided with important features to characterise the symptoms of a macular degeneration.

关键词： image enhancement image colour analysis data analysis histogram specification generalised extreme value distribution luminance channel digital colour retina images colour bands visual diagnosis age-related macular degeneration brightness contrast colour balance location parameter shape parameters algorithm performance image datasets

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Recent review on image clustering

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IET image PROCESSING 2015年第11期9卷 1020-1032页

作者： Ahmed, Nasir Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan

In this review, image clustering problem is discussed starting from global learning based clustering approaches such as Kmeans to the recent challenges in this domain. In global learning based clustering models, cluster evaluation criteria was formulated on whole image datasets. In local learing based clustering models, local neighbourhood information in image data matrices were utilised. However, performances of local learning based clustering models may face limitations for image datasets that contain images with pose, illumination, or occlusion variations. Further, both global learning and local learning approaches were combined in clustering models. Still, image clustering performances are not significantly improved. I elaborate challenges in image clustering problem by categorising image datasets as Gaussian-like or multimodal. Clustering performances on 14 benchmark image datasets of almost all state-of-the-art clustering models are optimal only for Gaussian-like image datasets. Thus, image clustering performance has direct correlation with the distribution of image datasets. Further, by employing optimal image descriptor, clustering performances are data dependent. Almost all exiting clustering models are based on second-order statistics. Owing to which, multimode image patterns may not be effectively handled even by exploiting both local and global information in image data matrices.

关键词： image processing reviews learning (artificial intelligence) review image clustering problem global learning based clustering approaches global learning based clustering models cluster evaluation criteria image datasets nonlinear manifold image data matrices illumination occlusion learning based clustering models image clustering performance second-order statistics multimode image patterns

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image Classification using Hybrid Data Mining Algorithms - A Review 2

Image Classification using Hybrid Data Mining Algorithms - A...

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2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Thamilselvan, P. Sathiaseelan, J. G. R. Bishop Heber Coll Dept Comp Sci Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479968183

Data mining is one of the most significant research area in computer science. It is a calculation process of finding and determining valuable information from huge data set. image classification is an important technique to generate valuable information. The classification method provides the accurate result in their target class. This review compares the some predominant hybrid classification algorithms to find the classification accuracy for various data sets and their performance of techniques. It provides some important hybrid techniques that have been used for image classification. In this paper the hybrid data mining algorithms are studied like GA-SVM, EKM-EELM, AdaBoost-SVM, Decision Tree-Naive Bayes, and SVM-CART.

关键词： Hybrid Approach image Classification image Mining image datasets Mining algorithms

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Feature Selection for Object Detection: The Best Group vs. the Group of Best 37th

Feature Selection for Object Detection: The Best Group vs. t...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Fuerst, Luka Leonardis, Ales Univ Ljubljana Fac Comp & Informat Sci SI-1000 Ljubljana Slovenia

ISBN: (纸本)9789532330816

The problem of visual object detection, the goal of which is to predict the locations and sizes of all objects of a given visual category (e. g., cars) in a given set of images, is often based on a possibly large set of local features, only a few of which might actually be useful for the given detection setup. Feature selection is concerned with finding a 'useful' subset of features. In this paper, we compare two approaches to feature selection in a visual object detection setup. One of them selects features based on their individual utility scores alone, regardless of possible interdependence with other features. The other approach employs the AdaBoost framework and hence implicitly deals with interdependence. Using two feature extraction methods and several image datasets, we experimentally confirm the significance of feature interdependence: features that perform well individually do not necessarily perform well as a group.

关键词： feature extraction learning (artificial intelligence) object detection AdaBoost framework detection setup feature extraction methods feature selection image datasets individual utility scores visual category visual object detection visual object detection setup Detection algorithms Educational institutions Feature extraction image segmentation Object detection Training Visualization Feature extraction Visualization image segmentation Object detection Functional training Detection algorithms Educational institutions

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SVM is not always confident: Telling whether the output from multiclass SVM is true or false by analysing its confidence values 16

SVM is not always confident: Telling whether the output from...

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2014 16th IEEE International Workshop on Multimedia Signal Processing, MMSP 2014

作者： Yamasaki, Toshihiko Maeda, Takaki Aizawa, Kiyoharu Department of Information and Communication Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781479958962

This paper presents an algorithm to distinguish whether the output label that is yielded from multiclass support vector machine (SVM) is true or false without knowing the answer. Such judgment is done only by the confidence analysis based on the pre-training/testing using the training data. Such true/false judgment is useful for refining the output labels. We experimentally demonstrate that the decision value difference between the top candidate and the second candidate is a good measure. In addition, a proper threshold can be determined by the pre-training/testing using only the training data. Experimental results using three standard image datasets demonstrate that our proposed algorithm can improve Matthews correlation coefficient (MCC) much better than simply thresholding the decision value for the top candidate.

关键词： data analysis support vector machines MCC Matthews correlation coefficient confidence analysis confidence values decision value difference decision value thresholding false judgment image datasets multiclass SVM multiclass support vector machine output label true judgment Accuracy Classification algorithms Machine learning algorithms Support vector machines Testing Training Training data Support Vector Network Training data Machine learning algorithms Sorting algorithm MCC Genes False multi-class Functional training

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Color and Size image Dataset Normalization Protocol for Natural image Classification: a Case Study in Tomato Crop Pathologies

Color and Size Image Dataset Normalization Protocol for Natu...

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Symposium of image, Signal Processing, and Artificial Vision

作者： Juan F. Molina Rodrigo Gil Carlos Bojacá Gloria Diaz Hugo Franco Student of Computer Engineering at the Universidad Central (Colombia) Centro de Biosistemas Universidad Jorge Tadeo Lozano (Colombia) Comp. Eng. Universidad Antonio Nari?o (Colombia) Comp. Eng. Universidad Central (Colombia)

ISBN: (纸本)9781479911196

In computer vision research, the construction of image datasets is a critical process, given the need for robust experimentation frameworks that ensure the quality and validity of the resulting conclusions and performance measurements in each particular study. Therefore, experimental datasets must optimize their statistical, visual and computational properties through an adequate selection of representative and useful visual data, according to the specific research question being addressed. This paper proposes a dataset construction protocol for ad hoc acquired images in a particular Machine Learning application: tomato crop health assessment.

关键词： Computer vision image retrieval natural images image datasets images computer vision image retrieval image classification Dataset ad hoc pathology protocols Case studies Tomatoes imagery (Psychotherapy) image

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An anthropomorphic polyvinyl alcohol brain phantom based on Colin27 for use in multimodal imaging

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MEDICAL PHYSICS 2012年第1期39卷 554-561页

作者： Chen, Sean Jy-Shyang Hellier, Pierre Marchal, Maud Gauvrit, Jean-Yves Carpentier, Romain Morandi, Xavier Collins, D. Louis McGill Univ Montreal Neurol Inst McConnell Brain Imaging Ctr Montreal PQ H3A 2B4 Canada INRIA Rennes EPI Serp IRISA F-35042 Rennes France INRIA Rennes IRISA INSA F-35042 Rennes France IRISA INRIA VisAGes U746 F-35042 Rennes France INSERM IRISA U746 F-35042 Rennes France Univ Rennes 1 F-35065 Rennes France

Purpose: In this paper, the method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) is proposed for validation of image processing methods such as segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms because of its mechanical similarities to soft tissues. Methods: The phantom was cast in a mold designed using the left hemisphere of the Colin27 brain dataset [C. Holmes et al., "Enhancement of MR images using registration for signal averaging," J. Comput. Assist. Tomogr. 22(2), 324 (1998)]. Marker spheres and inflatable catheters were also implanted to enable good registration comparisons and to simulate tissue deformation, respectively. Results: The phantom contained deep sulci, a complete insular region, and an anatomically accurate left ventricle. It was found to provide good contrast in triple modality imaging, consisting of computed tomography, ultrasound, and magnetic resonance imaging. Multiple sets of multimodal data were acquired from this phantom. Conclusions: The methods for building the anatomically accurate, multimodality phantom were described in this work. All multimodal data are made available freely to the image processing community (http://***). We believe the phantom images could allow for the validation and further aid in the development of novel medical image processing techniques. VC 2012 American Association of Physicists in Medicine. [DOI: 10.1118/1.3673069]

关键词： PVA brain phantom Colin27 anthropomorphic image datasets

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