基於卷積神經網路的電影海報概念分析

Abstract

近年來現代人擁有多樣化的休閒娛樂方式，觀賞電影依舊是許多人的首要選擇，電影海報則扮演電影宣傳方式的重要角色，其設計者會以視覺上多樣的元素製作出符合影片風格、概念且具有吸引力的畫面，而這些視覺上的設計元素會與電影息息相關。人們能夠透過視覺輕易地從海報感受出電影的概念，而這些在海報中讓我們可以依據視覺感官接收到的電影海報概念可能有些甚麼？本論文假設電影海報設計的模式與電影的類型有相當程度的關係，在相似的電影類型中，會使用相同的電影海報設計元素。我們從IMP Awards網站上收集了近十年(2006─2015年)的電影海報作為研究的資料集，並從IMDb網站上取得各部電影的類型資訊及關鍵字。我們利用對於圖像辨識有優秀結果的卷積神經網路(Convolutional Neural Network)技術來取出電影海報中的特徵，並以電影關鍵字和情緒視為電影海報概念來分析其記錄大量影像特徵的神經元是否會與其之間有關聯性存在。在本論文的實驗結果發現，利用卷積神經網路對電影海報作電影類型之多標籤分類有良好的分類結果，而且Fc7層取出的特徵向量維度並不影響分類之效能。然而，以電影關鍵字和情緒視為電影海報概念之分析的部分，實驗顯示以本論文的方式進行分析，其與神經元的值之間的關聯性不明顯。In recent years, people have a variety of entertainments; however, watching movies is still the primary choice of many people. Movie posters are playing an important role in advertising a film. People can easily capture the concepts of a poster based on the visual cues it reveals. But, what exactly are the concepts? In this paper, we assume that the design of a movie poster is related to the movie genre; in other words, movies of the same genre may use a similar style in designing the movie posters. We collect movie posters from the IMP Awards website released during 2006 to 2015 as a study case and obtain the genres and keywords of each movie from the IMDb website. We use the Convolutional Neural Network as the main analysis technique, which has shown excellent performances on image recognition, to extract the features (neuron values) of a movie poster. Finally, we analyze the correlation between neuron values and keywords (and emotions), which are considered concepts a movie poster may have. Our study shows that using Convolutional Neural Network for classifying movie posters has a great performance, and the dimension of the Fc7 layer doesn’t affect the classification effectiveness. However, the correlation between neuron values and keywords (and emotions) is not obvious using the analysis approaches proposed in this thesis.