I have watched the James Bond series several times in chronological order (by release date). But what week of the year should you start watching the series so that: 1. Summer-y episodes fall in the summer months (Example: Dr. No, The Living Daylights) 2. Snow filled episodes are watched during winter months (Example: On Her Majesty Secret Service, For Your Eyes Only)
There are 25 movies in the series and I enjoy watching at most 1 per week. While it is impossible to have a perfect alignment of movie to season, there should be one that is the best.
Discretization & Labeling
To solve this problem I settled on counting number of scenes that had weather that could be considered "hot", "neutral", or "cold." Scene extraction is not a trivial process, so I sampled a single frame every 150 frames from each movie. This resulted in a total of 1270 frames to be annotated. To make it easier I used Amazon's Mechanical Turk (MTurk) to first annotate each frame about whether it was set indoors, outdoors, or neither. While the results were not 100% accurate, I decided that precision could always be increased later. I then manually annotated each outdoor frame with the labels: "hot", "neutral", or "cold." To be more specific:
- Hot is a scene where people seem hot, usually a desert or tropical setting.
- Neutral is a catchall category for outdoor scenes that are neither hot nor cold, think Spring and Fall weather.
- Cold means either snow or ice is in the scene and people are wearing winter clothing.
The result is that there are 156 "hot" frames, 53 "neutral" frames, and 26 "cold" frames. The hardest distinction is between "hot" and "neutral" frames - I believe many "hot" scenes should have been labeled as "neutral".
With the frame labeling complete I generated summaries for each movie with each frame annotated like the one below:
Each movie is represented by an array where each sampled frame has one of 4 values: 'h' (for "hot"), 'n' (for "neutral"), 'c' (for "cold"), and null for frames where weather could not be determined (or the scene was indoors).
There are several ways to summarize each movie into a feature vector. For now, I'm using the trivial way that is by counting the number of "hot", "neutral", and "cold" frames in each movie and dividing it by the total number of sampled frames for the movie. An extension is to weigh the significance of each label because winter scenes tend to be really important in Bond movies. Here is what the feature vectors look like when visualized next to each other.
This is probably a good time to pause and formalize the solution I'm looking for. What is the best week of the year to start watching the Bond series so the weather in the movies match the current weather. If this sounds weird just think: Dr. No is a great movie during the summer and an excuse to drink Red Stripe. On the other hand On Her Majesty's Secret Service takes place during Christmas and it would be nice to watch that movie around December. Here are some rules and ideas to consider: - Movies are always watched in order of their release - Movies are watched at most 1 per week - Option: Movies do not need to be watched every week, there could be breaks between each movie watched
In the next part I'll discuss how I represented the weather for any given week of the year in New York. Also, first results of trying to find the best time of the year to begin watching the Bond series!