4 Pics 1 Word 8 Letters Answers List
This article was co-authored by staff writer, Nicole Levine, MFA. Nicole Levine is a Technology Writer and Editor for . She has over 20 years of experience creating technical documentation and leading support teams at leading web hosting and software companies. Nicole also holds an MFA in Creative Writing from Portland State University and teaches composition, fiction writing, and creative writing at various institutions.
4 Pics 1 Word 8 Letters Answers List
This teaches you the basics of playing 4 Pics 1 Word, a free word association game for your Android, iPhone or iPad. In this game, you will be shown 4 photos in a grid, all of which have a common word. Your goal is to figure out the common word based on a word length that is provided to you and a choice of possible letters. Although the main features of the game can be played alone, you can also challenge your friends to a game (once you’ve both reached level 20).
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This article was co-authored by staff writer, Nicole Levine, MFA. Nicole Levine is a Technology Writer and Editor for . She has over 20 years of experience creating technical documentation and leading support teams at leading web hosting and software companies. Nicole also holds an MFA in Creative Writing from Portland State University and teaches composition, fiction writing, and creative writing at various institutions. This article has been viewed 119, 130 times.Jetpac is building a modern version of Yelp, using Big Data rather than user reviews. People take billions of photos every day, and many of those are shared publicly on social media. We analyze these images to build better descriptions of bars, restaurants, hotels and other places around the world.
When you see a tag like “Hipsters” in the app, you probably wonder where it comes from. The short answer is that we are seeing places that have a lot of mustaches! There’s a lot going on under the hood to get to that conclusion, and we’ve had fun building some pretty unusual algorithms, so I’m going to get a little excited about how we do it.
One thing to keep in mind as we dig into this is that we’re in engineering, not research, so our goal is to build tools that meet our needs, rather than trying to do basic science. While I’ve included the results of our internal testing, nothing here has gone through rigorous peer review, so use our conclusions with caution. The ultimate proof is in the app, which I’m damn proud of, so please download it and see for yourself!
The most important information we extract is from the image pixels. These tell us a lot about the places and people that are in the photos, especially since we have hundreds or thousands of images for most places.
Pics 1 Word Answers
One very important difference between what we do with Big Data and traditional computer vision applications is that we can tolerate much more noise in our recognition tests. We try to analyze the properties of one object (a bar for example) based on hundreds of images taken there. That means we can afford some errors in whether we think an individual photo is a match, as long as the errors are random enough to cancel out over such sample sizes. For example, we only see 18% of actual mustaches, and we mistakenly think that 1.5% of the clean-shaven people we see have facial hair. This would be useless for making a decision about an individual photo, but it is very effective at categorizing a population of people.
Imagine one bar that has a hundred photos of people, and in fact none of them have mustaches. We’ll probably see one or two incorrectly labeled as having a mustache, giving a mustache estimate of 0.01 or 0.02. Now imagine another bar where 25 of the hundred people have mustaches. We’ll see four or five of those mustaches, along with probably one errant nude face, giving a mustache rating of 0.05 or 0.06.
This might sound like a scam, and it is, but useful! Completely accurate computer vision is an AI-complete problem, but just like in language translation, the combination of heuristics and a large number of samples offers an effective alternative to the traditional semantic approach.
We also put together some very accurate individual tests, such as food and sky detection, which are good enough to use to create slideshows, an application where false positives are much more annoying as part of the experience, but the key point is. that we are able to use a much wider range of algorithms than traditional object recognition approaches. Since we are focused on using them for data, any algorithm with a decent correlation to the underlying property helps, even if it would be too noisy to use to return search results.
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Internally, we use a library of several thousand images that we’ve manually tagged with the attributes we care about as a development set to help us build our algorithms, and then a different set of a thousand or more to validate our results. All the numbers are based on that training, and I’ve included grids of a hundred random images to show the results visually.
We are interested in how well our algorithms correlate with the underlying property they are trying to measure, so we used the Matthews Correlation Coefficient (MCC) to assess how well they perform. I considered using precision and recall, but these ignore all negative results that are correctly rejected, which is the right approach to evaluate search results you present to users, but is not as useful as correlation measurement for a binary classifier. The full test result numbers are up as a Google spreadsheet, but I cite the MCC in the text as our primary metric.
We first find likely faces in a photo using image recognition, and then we analyze the upper lip to determine whether there is a mustache or other facial hair, with an MCC of 0.29. The false positives tend to be cases where there is strong overhead lighting, giving a dark shadow under people’s noses. We use the spread of mustaches to estimate how many hipsters live in an area.
Once we’ve found faces, we run pattern recognition to look for mouths that appear to be smiling. We’re looking for toothy smiles, rather than more subtle giggles. The metric gives us an MCC of 0.41. The measurement we come up with is actually the number of pixels in the image that we detect as part of a smile, so big smiles carry more weight than smaller ones. We use the number of smiles to gauge how good a time people are having in a place.
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We look for an area of bright red color in the lower half of any faces we detect. We have an MCC of 0.36, with some of the false positives caused by people with naturally red lips. The amount of lipstick found is used to calculate how classy and fancy a bar or club is.
We run an algorithm that looks for plates or cups that take up most of the photo. It is quite selective, with a precision of 0.78, but a recall of only 0.15, and an MCC of 0.32. If a lot of people are taking pictures of their meals or coffee, we assume that there is something remarkable about what is being served and that it is popular with diners.
We look about half the height of a head below a detected face, and see how large a contiguous area of skin-colored pixels is exposed. This will detect bare breasts, and low-cut dresses, with the value we output corresponding to how much skin is exposed. We use this measure to assess how risky a bar or nightclub is. Pink sweaters and other items at chest height can easily cause false positives, so a large enough sample is needed to have a lot of confidence in the results.
The skin detection algorithm is simple, looking at which pixels are within a particular “flesh-toned” shade. This simplicity makes it prone to identifying things like beige walls and parchment menus as skin too unfortunately.
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We scan across the top of the photo, looking for areas that have the color and texture of a blue sky. This misses out on photos taken on cloudy days, sunsets, and can be confused by blue ceilings, but has proven effective in judging how scenic a place is, and whether a bar has an outdoor area. Our tests show that we get an MCC of 0.84.
This measures what proportion of the hue circle is present in the image. It looks at how many different colors are present, rather than how saturated any single color is, trying to find images with a rich range of colors rather than just being crappy. Since this is a continuous quality, we don’t have true and false positive statistics, but here are the results of applying it to a hundred random images, with the most colorful images on the top left, and moving left to right down the image.