Abstract
A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.
Highlights
Computer vision has vastly improved throughout the years
Because the construction of movie posters by genre was established in the late twentieth century, movies from the late twentieth century to the early twenty-first century could increase the accuracy of poster-based genre categorization if they make up the majority of the dataset
The project employed a dataset of 36,423 movie posters from movies published between 1913 and 2019
Summary
Computer vision has vastly improved throughout the years. During this time, the introduction of convolutional neural networks has made computer vision tasks such as tailored multimedia content synthesis considerably easier.[1,2] Despite this, determining the sort of movie by looking at the posters on the neural network has not proven very successful. Trailers provide detailed information in the form of videos, but due to their vast data size, they demand a lot of processing resources to classify the movie genre.[2] Movie posters, on the other hand, are created as a single image with tiny size and ratio, using less processing resources and being easier to produce. Using still photos taken from the source video, create raw artwork Netflix assesses these photographs to ensure that they appropriately represent your content in terms of aesthetics, ingenuity, and variety of items. This results in meaningful, individualized artwork depending on the interests of each member.[7,8,9] The method we propose addresses similar challenges by categorizing photographs into distinct genres based on aesthetics.
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