Image Recognition with MapReduce Based Convolutional Neural Networks

Leung, Jackie; Chen, Min

doi:10.1109/uemcon47517.2019.8992932

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. MapReduce [12] is one of the most efficient big data solutions, which enables the processing of a massive volume of data in parallel with many low-end computing nodes. This programming paradigm is a scalable and fault-tolerant data processing tool that was developed to provide significant improvements in large-scale data-intensive applications in clusters.…”

Section: Discussionmentioning

confidence: 99%

“…CNNs are innately both data and computationally intensive which make speed and storage capacity a large limiting factor in reaching performance and scalability requirements. To overcome the imposed time and space obstacles, this work implements a parallelized CNN algorithm based on MapReduce [12] (MCNN) on a cloud computing cluster. The developed algorithm takes advantage of the computational structures inherent in CNNs that lends them to parallelization to achieve increased processing speed.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Image Classification With Convolutional Neural Networks In MapReduce

Chen

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) techniques, more specifically Convolutional Neural Networks (CNNs), have become increasingly popular in advancing the field of data science and have had great successes in a wide array of applications including computer vision, speech, natural language processing and etc. However, the training process of CNNs is computationally intensive and high computational cost, especially when the dataset is huge. To overcome these obstacles, this paper takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. MapReduce is a scalable and fault-tolerant data processing tool that was developed to provide significant improvements in large-scale data-intensive applications in clusters. A MapReduce-based CNN (MCNN) is developed in this work to tackle the task of image classification. In addition, the proposed MCNN adopted the idea of adding dropout layers in the networks to tackle the overfitting problem. Close examination of the implementation of MCNN as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Image Classification With Convolutional Neural Networks In MapReduce

Chen

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A Deep Learning Approach for Diagnosis and Detection of Acute lymphocytic leukemia

El-Shashwati,

El-Demerdash,

Mohammed

2023

2023 Intelligent Methods, Systems, and Applications (IMSA)

View full text Add to dashboard Cite

Hypericons for Abstract Concepts: Non-traditional Approaches to Visual Explainability

Pandiani

Lazzari

Erp

et al. 2023

Preprint

View full text Add to dashboard Cite

In the cultural heritage (CH) field, abstract concepts–such as comfort, power, or freedom–are considered powerful tools to index cultural visual data. Simultaneously, the ever-increasing number of images is too great to index manually, so computer vision (CV)-based methods are employed to automatically classify images. The CV field, however, has paid scarce attention to image classification based on abstract concepts, partly because it has historically favored tasks and datasets for purportedly objective, concrete categories. More subjective and culturally-coded classes, like abstract concepts, more explicitly require interpretability of predictions by automatic models, given the potential to echo harmful bias. This is especially true for their detection in CH collections, given that they tend to contain many `ethically sensitive' depictions. To address the gap in CV-based interpretable methods for automatic classification of abstract concepts, we (1) present ARTstract, a dataset of cultural images and their evocation of certain abstract concepts (2) report baseline model performances on ARTstract for the task of image classification by abstract concepts, and, critically, (3) use them as a case study of traditional and non-traditional approaches to visual interpretability, inspired by [Offert \& Bell 2021]’s work. We introduce a unique approach to testing the interpretability of predictions by automatic models, using a combination of attribution maps (AM) and stable diffusion (SD). This approach has not been attempted before, and we experiment with it as a way to extend hermeneutic work back into the technical system that enables, unveiling its potential as a provider of lessons for developing systems that are interpretable-by-design.

show abstract

Image Recognition with MapReduce Based Convolutional Neural Networks

Cited by 3 publications

References 11 publications

Image Classification With Convolutional Neural Networks In MapReduce

Image Classification With Convolutional Neural Networks In MapReduce

A Deep Learning Approach for Diagnosis and Detection of Acute lymphocytic leukemia

Hypericons for Abstract Concepts: Non-traditional Approaches to Visual Explainability

Contact Info

Product

Resources

About