Journal ArticleDOI
Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning
TLDR
Experimental results show that the proposed method performs well on the classification of insect species, and outperforms the state-of-the-art methods of the generic insect categorization.About:
This article is published in Computers and Electronics in Agriculture.The article was published on 2015-11-01. It has received 107 citations till now. The article focuses on the topics: Multiple kernel learning & Sparse approximation.read more
Citations
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Journal ArticleDOI
Pest identification via deep residual learning in complex background
TL;DR: To achieve pest identification with the complex farmland background, a pest identification method is proposed that uses deep residual learning that has a high value of practical application, and can be integrated with currently used agricultural networking systems into actual agricultural pest control tasks.
Journal ArticleDOI
Crop pest classification based on deep convolutional neural network and transfer learning
TL;DR: The results demonstrated that the proposed CNN model is effective in classifying various types of insects in field crops than pre-trained models and can be implemented in the agriculture sector for crop protection.
Proceedings ArticleDOI
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
TL;DR: This paper collects a large-scale dataset named IP102, which contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution and has the challenges of interand intra- class variance and data imbalance.
Journal ArticleDOI
A survey on image-based insect classification
Maxime Martineau,Donatello Conte,Romain Raveaux,Ingrid Arnault,Damien Munier,Gilles Venturini +5 more
TL;DR: This survey investigates fourty-four studies on image-based insect recognition and tries to give a global picture on what are the scientific locks and how the problem was addressed.
Journal ArticleDOI
Insect Detection and Classification Based on an Improved Convolutional Neural Network.
TL;DR: Experimental results show that the proposed convolutional neural network model for multi-classification of crop insects achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
BookDOI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Journal ArticleDOI
Color indexing
Michael J. Swain,Dana H. Ballard +1 more
TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.