Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects

Larios, Natalia; Deng, Hucheng; Zhang, Wei; Sarpola, Matt J.; Yuen, Jenny; Paasch, Robert; Moldenke, Andrew R.; Lytle, David A.; Ruíz-Correa, Salvador; Mortensen, Eric N.; Shapiro, Linda G.; Dietterich, Thomas G.

doi:10.1007/s00138-007-0086-y

Cited by 116 publications

(76 citation statements)

References 24 publications

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“…We obtain the best bag-of-features histogram corresponding to each feature and then concatenate them together. This histogram is then renormalized and used as a combined feature [62] for classification. This method performed best for the Hollywood movie actions dataset and comparably for the other datasets.…”

Section: Resultsmentioning

confidence: 99%

Action recognition using global spatio-temporal features derived from sparse representations

Somasundaram

Cherian

Morellas

et al. 2014

Computer Vision and Image Understanding

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Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to alleviate this difficulty is to focus the computations to informative (salient) regions of the video. In this paper, we propose a novel global spatio-temporal selfsimilarity measure to score saliency using the ideas of dictionary learning and sparse coding. In contrast to existing methods that use local spatio-temporal feature detectors along with descriptors (such as HOG, HOG3D, HOF, etc.), dictionary learning helps consider the saliency in a global setting (on the entire video) in a computationally efficient way. We consider only a small percentage of the most salient (least self-similar) regions found using our algorithm, over which spatio-temporal descriptors such as HOG and region covariance descriptors are computed. The ensemble of such block descriptors in a bag-of-features framework provides a holistic description of the motion sequence which can be used in a classification setting. Experiments on several benchmark datasets in video based action classification demonstrate that our approach performs competitively to the state of the art.

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Section: Resultsmentioning

confidence: 99%

Action recognition using global spatio-temporal features derived from sparse representations

Somasundaram

Cherian

Morellas

et al. 2014

Computer Vision and Image Understanding

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“…There is a growing body of work investigating finegrained image classification of birds [8,24,27], insects [15,18], flowers [6,19] and leaves [1,6,14].…”

Section: Related Workmentioning

confidence: 99%

Vantage Feature Frames for Fine-Grained Categorization

Sfar

Boujemaa

Geman

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…These images may not be suitable in real-time environments. In [12], author explained the uses of ANN and Bayesian, for correctly identifying a rice fields pest named as Yellow Stem Borer. They have used a dataset specifically designed for the rice field which was compiled by collecting the data over the duration of 12 years, for different climates.…”

Section: Related Workmentioning

confidence: 99%

K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

Javed¹,

Noor²,

Khan³

et al. 2017

ijacsa

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Abstract-Agriculture is the backbone to the living being that plays a vital role to country's economy. Agriculture production is inversely affected by pest infestation and plant diseases. Plants vitality is directly affected by the pests as poor or abnormal. Automatic pest detection and classification is an essential research phenomenon, as early detection and classification of pests as they appear on the plants may lead to minimizing the loss of production. This study puts forth a comprehensive model that would facilitate the detection and classification of the pests by using Artificial Neural Network (ANN). In this approach, the image has been segmented from the fields by using enhanced K-Mean segmentation technique that identifies the pests or any object from the image. Subsequently, features will be extracted by using Discrete Cosine Transform (DCT) and classified using ANN to classify pests. The proposed approach is verified for five pests that exhibited 94% effectiveness while classifying the pests.

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Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects

Cited by 116 publications

References 24 publications

Action recognition using global spatio-temporal features derived from sparse representations

Action recognition using global spatio-temporal features derived from sparse representations

Vantage Feature Frames for Fine-Grained Categorization

K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

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