Object Classification Using Heterogeneous Co-occurrence Features

Ito, Satoshi; Kubota, Susumu

doi:10.1007/978-3-642-15552-9_16

Cited by 20 publications

(5 citation statements)

References 14 publications

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“…It is similar to flower image classification because of varieties of flower images, having similarities in various groups. Satoshi et al implemented a color classification of an object that utilized three heterogeneous co-occurrence colors such as CoHD, CoHOG, CoHED [27]. The results produced by that method efficiently achieve a high classification rate due to high dimensional and highly discriminating co-occurrence features.…”

Section: Classificationmentioning

confidence: 99%

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

Rathore

Jaiswal

Sharma

et al. 2021

Preprint

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Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input of different flower images from data sets, then converts it from RGB (Red, Green, Blue) color model to the L*a*b color model. L*a*b has reduced the effort of image segmentation. The flower image segmentation has been performed by the canny edge detection algorithm which provided better results. The implemented extended deep learning convolution neural network can accurately recognize varieties of flower images. The learning accuracy of the proposed hybrid method is up to 98% that is maximizing up to + 1.89% from state of the art.

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Section: Classificationmentioning

confidence: 99%

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

Rathore

Jaiswal

Sharma

et al. 2021

Preprint

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“…Mean Accuracy (%) Combined CoHoG [16] 74.80 Combined Features [22] 76.30 BiCoS-MT [5] 80.00 Det+Seg [1] 80.66 TriCoS [7] 85.20 GT# [23] 85 4 illustrates the changing trend of test mean accuracy as the number of stages increases. We observe that MsML converges very fast, which verifies that multi-stage division is essential to the proposed framework.…”

Section: Methodsmentioning

confidence: 99%

Fine-grained visual categorization via multi-stage metric learning

Qian

Jin

Zhu

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

123

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Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues: 1) many subordinate classes are highly correlated and are difficult to distinguish, and 2) there exists the large intra-class variation (e.g., due to object pose). This paper proposes to explicitly address the above two issues via distance metric learning (DML). DML addresses the first issue by learning an embedding so that data points from the same class will be pulled together while those from different classes should be pushed apart from each other; and it addresses the second issue by allowing the flexibility that only a portion of the neighbors (not all data points) from the same class need to be pulled together. However, feature representation of an image is often high dimensional, and DML is known to have difficulty in dealing with high dimensional feature vectors since it would require O(d 2 ) for storage and O(d 3 ) for optimization. To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving O(d) computational complexity. The empirical study with FVGC benchmark datasets verifies that our method is both effective and efficient compared to the state-of-the-art FGVC approaches.

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“…Studies working with unsegmented images show lower accuracy in classification and require powerful classifiers, which are computationally expensive [14,15]. Khan's work [16] relies on color and shape, using a SIFT-based approach.…”

Section: Related Workmentioning

confidence: 99%

Fusing well-crafted feature descriptors for efficient fine-grained classification

Mottos

Feris²

2014

2014 IEEE International Conference on Image Processing (ICIP)

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As citizen science projects become more popular and engage an increasing number of volunteers, smartphones are turning into commonly used sensors in the biodiversity environment. In this paper, we propose a novel approach for classification of subordinate categories such as plant and insect species that is fast and suitable for use in mobile devices. In particular, we show that a combination of carefully designed features, including a robust shape descriptor to capture fine morphological structures of objects, as well as traditional color and texture features, is essential for obtaining good performance. A novel weighting technique assigns different costs to each feature, taking into account the inter-class and intra-class variation between species. We tested our proposed method in the popular Oxford Flower Dataset and in the Leeds Butterfly Dataset. We are able to achieve state-of-the-art accuracy while proposing an efficient approach that is suitable for mobile applications and can be applied to different species.

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Object Classification Using Heterogeneous Co-occurrence Features

Cited by 20 publications

References 14 publications

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

Fine-grained visual categorization via multi-stage metric learning

Fusing well-crafted feature descriptors for efficient fine-grained classification

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Object Classification Using Heterogeneous Co-occurrence Features

Cited by 20 publications

References 14 publications

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation &amp; Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation &amp; Recognition with extended Deep-Convolution Neural Network (CNN)

Fine-grained visual categorization via multi-stage metric learning

Fusing well-crafted feature descriptors for efficient fine-grained classification

Contact Info

Product

Resources

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WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)