Project Title:

"Advanced Image Recognition Techniques: Feature Extraction, Enhancement, and

Ensemble Methods"

Objective:

Explore and compare the effectiveness of various feature extraction techniques in image

recognition tasks, both before and after image enhancement, with a focus on ensemble methods.

Part $1$: Image Enhancement and Feature Extraction

$1.$ Dataset Selection:

$$ Teams must select a dataset after researching various fields of image recognition.

The dataset should be challenging yet manageable $($like: face recognition,

fingerprint recognition, medical image diagnosis, etc.$)$

$$ Consider datasets from sources like Kaggle, UCI Machine Learning Repository, or

create a custom dataset if applicable.

$2.$ Feature Extraction Techniques:

$$ Implement at least three feature extraction methods: like LBP$,$ HOG, and SIFT.

$$ Each team member could focus on one technique to ensure balanced participation.

$$ Provide a brief theoretical background of each method in the report and Jupyter

Notebook.

$3.$ Image Enhancement:

$$ Apply at least two different image enhancement techniques such as histogram

equalization, noise reduction, or sharpening filters.

$$ Document the rationale behind choosing specific enhancement techniques and their expected impact on the image quality and feature extraction.

$4.$ Comparative Analysis:

$$ Analyze how feature extraction results vary before and after image enhancement.

$$ Use visual aids like plots or image grids in the Jupyter Notebook to demonstrate the

differences.

Part $2$: Classification and Ensemble Methods

$1.$ Classification Model Implementation:

$$ Implement a classification model suitable for the dataset. This could include models

like SVM$,$ Random Forest, or neural networks.

$$ Document the model selection process, including any parameter tuning or

optimization techniques used.

$2.$ Ensemble Techniques $($Bonus$)$:

$$ Combine different feature extraction techniques using ensemble methods like

stacking or voting classifiers.

$$ Compare the classification performance using individual features versus the

ensemble approach.

$$ Extra marks are awarded for innovative ensemble strategies and clear demonstration of their effectiveness.

$3.$ Evaluation and Comparative Analysis:

$$ Use metrics like accuracy, precision, recall, and F$1-$score to evaluate the

classification models.

$$ Discuss the impact of image enhancement on the classification results.

Additional Evaluation Criteria:

$$ Teamwork and Collaboration:

$$ Regular meeting minutes or logs should be maintained to track contributions and

collaboration.

$$ A section in the report should be dedicated to explaining each member's role and

contribution.

Part $3$: Jupyter Notebook as Project Report

$$ Include code, comments, and in$-$line analysis. Visualizations should be clear and welllabeled.

$$ The notebook should be executable without errors.

$$ Structured as follows:

$1.$ Project Overview: Briefly describe the objective and scope of the project.

$2.$ Team Information: List the team members and their respective roles.

$3.$ Dataset Description: Introduce the chosen dataset and justify its selection.

$4.$ Methodology

$5.$ Classification Model Implementation

$6.$ Ensemble Techniques $($Bonus Section$)$

$7.$ Comparative Analysis

$8.$ Conclusion

$9.$ References

$10.$ Appendices $($Optional$)$

Project Support:

$$ Instructor Availability:

$$ Regular office hours for project$-$related queries.

$$ Feedback on initial project proposal and dataset selection.

$$ Learning Resources:

$$ Supplementary materials on Python, Jupyter Notebooks or VSCode, and specific

image processing techniques will be provided.