README.md

Guess the Genre 🎞️

Anyone may have misunderstood the movie genre only by looking at movie posters. We wondered if machine learning and deep learning could accurately predict the genre of the movie only by looking at the poster. Therefore, using machine learning and deep learning, respectively, we created models that predict the movie genre based on posters.

Dataset

• Kaggle Movie Genre from its Poster
• Before: IMDB ID / IMDB link / Title / IMDB Score / Genre / Poster → Delete a row if Poster is empty or invalid
• After: Title / Genre / Poster ({IMDB_ID}.jpg)

Deep Learning

Genre	#	Genre	#	Genre	#	Genre	#
Action	4,705	Adult	8	Adventure	3,399	Animation	1,558
Biography	1,775	Comedy	11,193	Crime	4,593	Documentary	3,371
Drama	3,371	Family	17,654	Fantasy	1,789	Film-Noir	318
Game-Show	1	History	1,280	Horror	3,544	Music	1,133
Musical	710	Mystery	2,092	News	77	Reality-TV	2
Romance	5,379	Sci-Fi	1,787	Short	851	Sport	629
Talk-Show	6	Thriller	4,251	War	1,027	Western	722

Machine Learning

Genre	#	Genre	#	Genre	#	Genre	#
Animation	679	Comedy	11,193	Family	1,035	Romance	2,926

• Due to the limiation of implementing a multi-label, we chose 1 genre for each movie (ex. Toystory: Animation, Adventure, Comedy, ... → Animation)
• Then, we reorganized the dataset by selecting 4 genres that show the biggest difference between feature vectors among 28 genres.

Model

Machine Learning: KNN

Machine Learning: Ensemble (KNN + SVM)

Deep Learning: Custom Model

Deep Learning: Fine-tuned Pre-trained VGG-16

Inference

Machine Learning: KNN

Machine Learning: Ensemble

Deep Learning: Custom Model

Deep Learning: Fine-tuned Pre-trained VGG-16

Comparison

Model	Train	Parameter	Structure Complexity	Dimension	K	Distance Metric	Memory	Accuracy
KNN	14.1s	28,000	O(N)	256	8	Euclidean distance	111,376 MB	70.92%
Ensemble	15.8s	56,003	O(N^3)	256	8	Euclidean distance	154,544 MB	71.69%

Model	Learning Rate	Epoch	Optimizer	Train	Parameter	Layer	Memory	Accuracy
Custom Model	0.0001	150	Adam	6h 33m 10s	505,660	13	141,856 MB	77.28%
VGG-16	0.0001	50	RMSprop	2h 37m 11s	27,327,324	5	571,264 MB	82.22%

• The accuracy of machine learning and deep learning are not dramatically different, but the performance of deep learning is much better. This is because machine learning currently uses 4 out of 28 labels, and only 1 label is offered as an inference value, and deep learning uses all 28 labels and is implemented as a multi-label.
• In machine learning, there is a limit to accurately predicting genres with only histograms, so we thought about various methods such as poster composition, number of people, and text. However, the extraction process was often more difficult than the machine learning algorithm itself, and even if the extraction was made, the accuracy did not change significantly. However, it was good that machine learning ended in seconds without turning it for hours like deep learning after storing it in memory.
• Deep learning required much more datasets than machine learning. It also took a lot of time even to use a local GPU, and memory was used much more. Also, the process of increasing accuracy by changing the parameters of the model was difficult. However, the pre-processing process was not difficult because deep learning did not require feature extraction. Since the output structure of the model can be set directly, it is very suitable for datasets that can have multi-label, such as movie posters.

Further Work

• Currently, the number of datasets for each label is different, and if the number of datasets for each label is unified and learned, both machine learning and deep learning are expected to have higher accuracy.
• Machine learning infers the majority of posters as comedy. Likewise, deep learning infers the majority of posters as drama. It is expected that different results will come out if they are trained except for the comedy/drama dataset.

Contributor

• Serin Yoon
• Dongmin Son

Reference

https://github.com/d-misra/Multi-label-movie-poster-genre-classification