Animal Classification System: A Block Based Approach

Kumar, Y. H. Sharath; Manohar, N; Chethan, H. K.

doi:10.1016/j.procs.2015.03.156

Cited by 27 publications

(10 citation statements)

References 16 publications

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“…CNN improves variation learning and can notice special features and better learn object representations, decreasing the error rate of detection of ResNet by 33%, Alexnet by 43%, and DenseNet by 42. Kumar [16] suggested a hybrid model using a probabilistic neural network (PNN) and K-nearest neighbors (KNN) for classification of animal images. The proposed model consisted of several phases, such as segmentation, extraction, and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Mohammed

Hussain

2021

Computation

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Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Mohammed

Hussain

2021

Computation

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“…The study of Wlodarczak [36] reviews few of the significant deep learning methods, recently adopted into the scope of multimedia and video data mining. In the study of Kumar [37], a method that efficiently classifies animal objects from images has been introduced. The study also emphasized on eliminating the background objects from a given image by using a graph cut based technique.…”

Section: Review Of Literaturesmentioning

confidence: 99%

A Systematic Review of Existing Data Mining Approaches Envisioned for Knowledge Discovery from Multimedia

S¹,

N²

2018

IJECE

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<p>The extensive use of multimedia technologies extended the applicability of information technology to a large extent which results enormous generation of complex multimedia contents over the internet. Therefore the number of multimedia contents available to the user is also exponentially increasing. In this digital era of the cloud-enabled Internet of Things (IoT), analysis of complex video and image data plays a crucial role.It aims to extract meaningful information as the distributed storages and processing elements within a bandwidth constraint network seek optimal solutions to increase the throughput along with an optimal trade-off between computational complexity and power consumption. However, due to complex characteristics of visual patterns and variations in video frames, it is not a trivial task to discover meaningful information and correlation. Hence, data mining has emerged as a field which has diverse aspects presently in extracting meaningful hidden patterns from the complex image and video data considering different pattern classification approach. The study mostly investigates the existing data-mining tools and their performance metric for the purpose of reviewing this research track.It also highlights the relationship between frequent patterns and discriminativefeatures associated with a video object. Finally, the study addresses the existing research issues to strengthen up the future direction of research towards video analytics and pattern recognition.</p>

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“…colour and pattern) for each specimen, facilitating measurement of multiple metrics relevant to our goal of testing the drivers of UV reflectance including mean, peak, and presence of UV colouration across the entire specimen. Segmentation is commonly used on biomedical images to separate focal regions such as cells, organs, and bones (Aljabar et al 2009;Baiker et al 2010;Meijering 2012) and is also beginning to be used more widely on digitised natural history datasets (Kumar et al 2015;Unger et al 2016). However, to be a truly scalable solution for thousands to potentially millions of images, segmentation methods must provide reliable output.…”

Section: Introductionmentioning

confidence: 99%

Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds

Cooney

Varley

et al. 2021

Preprint

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Ultraviolet (UV) colouration is thought to be an important signalling mechanism in many bird species, yet broad insights regarding the prevalence of UV plumage colouration and the factors promoting its evolution are currently lacking. Here, we develop a novel image segmentation pipeline based on deep learning that considerably outperforms classical (i.e. non-deep learning) segmentation methods, and use this to extract accurate information on whole-body plumage colouration from photographs of >24,000 museum specimens covering >4,500 species of passerine birds. Our results demonstrate that UV reflectance, particularly as a component of other colours, is widespread across the passerine radiation but is strongly phylogenetically conserved. We also find clear evidence in support of the role of light environment in promoting the evolution of UV plumage colouration, and a weak trend towards higher UV plumage reflectance among bird species with ultraviolet rather than violet-sensitive visual systems. Overall, our study provides important broad-scale insight into an enigmatic component of avian colouration, as well as demonstrating that deep learning has considerable promise for allowing new data to be bought to bare on long-standing questions in ecology and evolution.

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Animal Classification System: A Block Based Approach

Cited by 27 publications

References 16 publications

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

A Systematic Review of Existing Data Mining Approaches Envisioned for Knowledge Discovery from Multimedia

Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds

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