Image Classification Based on Deep Learning

Hanyang Tan

FEIT, UTS, Sydney, Australia

Keywords: DDN, CNN, CIFAR 10.

Abstract: Image classification technology, as a core research direction in the field of computer vision, has become the

focus of widespread attention among researchers with the development of deep learning technology. Although

convolutional neural networks (CNN) have made revolutionary progress in image processing, there are still

problems such as overfitting and the complexity of handling diverse data sets. This paper presents a hybrid

model composed of a Convolutional Neural Network (CNN) module and a time-frequency composite

weighting module. The CNN module effectively performs deep feature extraction, while the time-frequency

composite weighting module is capable of achieving better performance. Through experimental verification

on CIFAR 10, this paper demonstrates the excellent performance of the hybrid model on image classification

tasks, with an accuracy of 90%. The results of this paper not only prove the effectiveness of combining

different deep learning architectures to improve image classification accuracy, but also provide new ideas and

methods for the development of future image processing technology.

1 INTRODUCTION

In recent years, machine learning-based data analysis

methods have achieved notable results in tasks

involving text, video, and audio. Image classification,

a fundamental technique within data analysis, plays a

crucial role in diverse applications spanning business,

military, and everyday life scenarios. In the early

stages of image classification research, the process

required the design of manual features based on the

characteristics of images, followed by classification

using machine learning models. For instance, features

such as color histograms and texture information were

extracted and then classified using machine learning

models like Support Vector Machines (SVMs) and

Decision Trees. Traditional machine learning

methods, characterized by a limited number of

parameters and a heavy reliance on the results of

manual feature extraction, significantly increased the

difficulty of model optimization. Fortunately, the

advent of deep learning has enabled the joint

optimization of feature extraction and classification

modules, representing a significant leap forward in

the field.

Image preprocessing mainly includes image

clipping, scaling and normalization, which ensures

the consistency of input data. In the feature extraction

stage, deep learning model is used to extract the deep

features in the image, and the image is encoded as a

feature vector. According to the input image features,

the classification module predicts the probability

distribution of the categories, which is usually

processed using the softmax function.

In the field of computer vision, deep learning has

emerged as a pivotal technology for advancing image

classification techniques. This research aims to

further enhance image classification performance by

integrating Convolutional Neural Networks (CNNs)

with Deep Decision Networks (DDNs). While CNNs

have revolutionized image processing with their

ability to autonomously extract hierarchical features,

this paper introduces an innovative hybrid model

designed to improve image classification accuracy.

The hybrid model combines the powerful feature

extraction capabilities of CNNs with the unique

decision-making perspective offered by DDNs,

aiming to create a more robust and adaptive image

classification system.

Specifically, the model proposed in this paper is

comprised of key components including

convolutional layers, fully connected layers,

activation functions, and a softmax layer. These

components work in concert to enhance the model's

ability to recognize various features in images, while

Tan, H.

Image Classiﬁcation Based on Deep Learning.

DOI: 10.5220/0012835500004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 313-317

ISBN: 978-989-758-690-3

313

the decision-making mechanism is optimized for the

classification process. In the experimental section,

this model was tested on the CIFAR-10 dataset, a

standard benchmark in the field. The results

demonstrate that the model achieved an accuracy rate

of 90% in the image classification task, showcasing

the effectiveness of our model.

2 RELATED WORK

Early image recognition relied on traditional feature

descriptor design, which usually required manual

feature extraction. These methods require domain

expertise to design and extract relevant features of

images, which greatly limits the efficiency of image

extraction. In addition, because the feature extraction

and classification models cannot perform parameter

optimization at the same time, the classification effect

is poor.

Deep learning has had a profound impact on

image recognition. It has significantly improved the

accuracy of recognition systems by automatically

learning relevant features from raw image data,

eliminating the need for handcrafted feature

engineering (Murthy et al. 2016). These models are

particularly effective in performing complex tasks by

optimizing parameters across both feature extraction

and downstream tasks simultaneously. Additionally,

the deep network structures are capable of extracting

high-level semantic features, which is fundamental in

understanding and interpreting complex image

content. Such capabilities have propelled deep

learning to the forefront of advancing technologies in

computer vision, enabling significant progress in

object detection, localization, semantic segmentation,

and image generation.

Venkatesh N. Murthy and Vivek Singh's (2013)

research introduces Deep Decision Networks (DDNs)

as a novel solution for image classification. DNNS

mainly alleviates the problem of gradient

disappearance or gradient explosion in deep networks

through phased training. By merging the

straightforward structure of decision trees with the

capabilities of deep learning, DDNs use decision

stumps at each node for initial classification and

allocate specialized nodes for more complex

scenarios. This strategy enhances the efficiency and

accuracy in handling large, varied datasets,

showcasing a significant advancement in the

approach to image classification challenges.

3 METHDOLOGY

In paper proposes an innovative hybrid deep learning

model that combines a convolutional neural network

(CNN) and a deep decision network (DDN), with the

aim of improving the performance of traditional

models in image recognition accuracy. First, all

emerging images were normalized and necessary

preprocessed. Then, the normalized data is used to

process it through DDN, and a new loss function is

introduced to improve the classification accuracy.

Then, CIFAR 10 was used for experimental

verification. In aligning with the pioneering

approaches for feature extraction, this study leverages

the insights from Su (2015), who demonstrated the

efficacy of multi-view convolutional neural networks

in recognizing complex 3D shapes by extracting

nuanced features that capture the essence of the

objects from various angles (Ciresan et al. 2012). This

principle of extracting deep, meaningful features

forms the cornerstone of our methodology, where the

CNN component of our hybrid model meticulously

learns to identify intricate patterns within the CIFAR-

10 dataset images. The ability of CNNs to discern and

learn from the dataset's diversity not only underscores

the adaptability of our model but also its potential to

generalize across different image classification tasks,

drawing from the foundation laid by the referenced

work in enhancing model performance through

sophisticated feature extraction techniques.

3.1 Normalizing Images

Normalization of images is an important

preprocessing step. The main purpose is to convert

image data into a more consistent range to facilitate

the training of neural networks. Normalization

usually involves two key steps: adjusting the mean

and standard deviation of the data. Normalization

refers to converting image data from the original pixel

value range (usually 0 to 255) to a smaller range (such

as -1 to 1 or 0 to 1) (Su et al. 2015). The main propose

to normalizing is to improve the stability and

convergence speed of model training. The method

used in the paper is based on "Z-score

standardization". Building upon the foundational

work of Simonyan and Vedaldi (Simonyan & Vedaldi

2013), who emphasized the critical role of deep

feature extraction in enhancing image classification

models, this study adheres to a rigorous normalization

process to ensure the consistency and reliability of

input data for neural network training (Su et al. 2015).

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3.2 Mixed Model

The methodology section introduces an innovative

approach by combining Convolutional Neural

Networks (CNNs) and Deep Decision Networks

(DDNs) to analyze the CIFAR-10 dataset. The model

employs CNN layers for robust feature extraction

from images, leveraging their capability to

autonomously learn and identify intricate patterns.

Next, the model simulates the process of a deep

decision network (DDN) through dense layers, a step

designed to make decisions based on features

extracted by the CNN. Inspired by the

groundbreaking work of Cireșan et al. and Zheng et

al., who showcased the significant improvements in

image classification accuracy through the use of

multi-column deep neural networks, our research

adopts a similar philosophy in enhancing the

robustness and accuracy of our hybrid model (Zheng

et al. 2021). This integration method not only takes

advantage of the powerful capabilities of CNN in

feature extraction, but also attempts to simulate the

advantages of DDN in decision-making efficiency,

thereby improving the classification accuracy of the

model. This hybrid architecture aims to enhance

classification accuracy by utilizing CNN's strength in

feature extraction and approximating DDN's

decision-making efficiency. The model is compiled

and trained with categorical cross-entropy loss and

Adam optimizer, evaluated to demonstrate its

effectiveness in image classification tasks.

4 EXPERIMENT AND RESULT

4.1 Datasets

The CIFAR-10 database (Krizhevsky), developed by

the Canadian Institute for Advanced Research, is a

standard test set widely used in computer vision

research. It contains 60,000 32x32 pixel color images

divided into 10 categories with 6,000 images in each

category. These categories include common objects

such as airplanes, cars, birds, cats, deer, dogs, frogs,

horses, boats, and trucks. Images in the database are

carefully selected and annotated to ensure an even

distribution of images within each category.

he CIFAR-10 images are filtered from the larger

80 million tiny images dataset, which contains about

80 million small images of 32x32 pixels. Each

category in the CIFAR-10 dataset is filtered from this

large dataset to ensure image quality and category

balance. In addition, the diversity and realism of

images in CIFAR-10 make it ideal for testing image

processing algorithms, especially when dealing with

common problems in real-world images, such as

changing lighting conditions, different viewing

angles, and background noise.

CIFAR-10 was originally designed to provide a

benchmark testing platform for computer vision

algorithms, especially for evaluating the performance

of image recognition and classification algorithms.

The dataset is divided into 50,000 training images and

10,000 test images to help researchers train and

validate their models.

To ensure data diversity and practicality, CIFAR-

10 images are collected from a variety of scenes and

backgrounds, covering a variety of lighting conditions

and postures. This database is not only highly

respected in academia, but also widely used in

industry, providing important data support for

improving the accuracy and robustness of image

processing technology.

The use of CIFAR-10 has greatly promoted the

development of the field of computer vision,

especially in the research of deep learning and

convolutional neural networks. It provides

researchers with a standardized platform to compare

the effects of different algorithms and inspires

innovation and progress in image recognition

technology by researchers around the world.

4.2 Results

When evaluated on the CIFAR-10 dataset, a

standard benchmark for computer vision, the model

showed a significant classification accuracy of 90%.

This performance metric emphasizes the model's

ability to accurately process and classify images

across different categories of the dataset. Our method

integrates the convolutional neural network with the

deep decision network, and makes full use of the

feature extraction capability of CNN and the decision-

making capability of DNN to improve the accuracy of

image classification.

5 DISCUSSION

In the current study, we successfully developed a

hybrid model combining convolutional neural

networks (CNN) and deep decision networks (DDN)

for image recognition tasks. This innovative attempt

not only marks the advancement of the application of

deep learning technology in the field of image

processing, but also demonstrates the huge potential

of cross-domain fusion technology. However,

although our model demonstrates excellent

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performance on multiple datasets, there are still a

series of challenges and opportunities to further

improve model efficiency, accuracy, and

interpretability. This chapter will discuss these

challenges in depth and explore possible future

research directions and technological improvement

paths, to promote scientific research and

technological innovation in this field.

As author contemplate the future trajectory and

potential enhancements for our hybrid model, the

integration of advanced rendering techniques and

contrastive learning principles, exemplified by the

works of Lassner and Zollhofer (2021) and Wang et

al. (2019), respectively, presents a compelling avenue

for innovation. The application of efficient sphere-

based neural rendering can significantly enrich the

visual representation and interpretability of images,

while adopting contrastive learning strategies from

the domain of long-tailed image classification

promises to address data imbalance and improve

classification accuracy across diverse datasets.

Moving forward, the exploration of these

methodologies, alongside the innovative strategies

suggested in References (Hinton et al. 2015) and

(Alzubaidi 2021), will be instrumental in overcoming

the current limitations of our model. By harnessing

these cutting-edge approaches, author aims to

enhance the model's robustness, adaptability, and

performance, ensuring its applicability to a broader

spectrum of image classification challenges and

setting a new benchmark for future research in the

field. Secondly, this paper also exposed the

interpretability shortcomings of deep learning

models. Although the model performed well on the

classification task, it was difficult to understand why

the model made the classification decision it did. This

lack of interpretability may limit the usefulness of the

model in certain application scenarios, especially

those that require a high degree of transparency and

interpretability.

Besides, In the pursuit of enhancing the efficiency

of our hybrid model, recent studies offer promising

methodologies that could be directly applicable. For

instance, leveraging advanced model compression

techniques, as discussed by (Hinton et al. 2015), can

significantly reduce the computational footprint of

deep learning models without compromising their

performance. This approach is critical for deploying

sophisticated models in resource-constrained

environments. Concurrently, the application of

Neural Architecture Search (NAS) methodologies,

exemplified in (Alzubaidi 2021), presents a strategic

pathway to automatically discover optimal model

architectures that balance accuracy with

computational efficiency. Integrating these cutting-

edge techniques promises not only to elevate the

operational efficiency of our hybrid model but also to

extend its applicability across a broader spectrum of

real-world scenarios, where computational resources

are often limited. Future iterations of our research will

explore these avenues, aiming to harness the potential

of (Hinton et al. 2015) and (Alzubaidi 2021) to

surmount current efficiency constraints, thereby

enhancing the model's viability for extensive

deployment.

In this paper, author explored the application of

deep learning technologies in image recognition by

integrating Convolutional Neural Networks (CNN)

and Deep Decision Networks (DDN). Recent

literature demonstrates the immense potential of deep

learning in handling complex tasks such as image

recognition and image caption generation.

Specifically, a review article (Hossain 2019) delves

into the challenges of deep learning, such as data

imbalance and model compression, as well as its

applications in fields like medical imaging.

Through continuous research and technological

innovation, we look forward to achieving broader and

more profound impacts in the fields of deep learning

and image recognition.

6 CONCLUSION

In this paper, we employ deep learning techniques for

image classification, specifically an architecture that

combines convolutional neural networks (CNN) and

deep decision networks (DDN). The experimental

results show that this hybrid model significantly

improves the accuracy and performance of image

recognition. However, in discussing these results, we

also recognize some key challenges and limitations.

First, although this model performs well on the

CIFAR-10 dataset, this does not mean that it can be

effective on all types of image recognition tasks. For

example, this model may have difficulty processing

more complex or irregular image data sets. Therefore,

future work may need to explore how to adapt and

optimize the model so that it can better handle various

types of image data.

Secondly, this paper also exposed the

interpretability shortcomings of deep learning

models. Although the model performed well on the

classification task, it was difficult to understand why

the model made the classification decision it did. This

lack of interpretability may limit the usefulness of the

model in certain application scenarios, especially

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those that require a high degree of transparency and

interpretability.

Finally, this paper also raises the issue of data

dependence of deep learning models. Although we

used CIFAR-10, a widely used standard dataset, this

also means that author’s results depend heavily on this

specific dataset. If the quality or representativeness of

the data set is insufficient, this model may not achieve

the same performance.

In summary, although author’s research has

achieved certain results in the field of image

classification, there are still many challenges and

issues that need to be addressed in future work.

Through further research and improvement, we

believe that the application of deep learning

technology in image recognition and other fields will

be more widespread and effective.

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