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To further that effort, today we are introducing similarity search on Flickr. In many ways, photo search is very different from traditional web or text search. First, the goal of web search is usually to satisfy a particular information need, while with photo search the goal is often one of discovery; as such, it should be delightful as well as functional.
We have taken this to heart throughout Flickr. Second, in traditional web search, the goal is usually to match documents to a set of keywords in the query.
That is, the query is in the same modality—text—as the documents being searched. Photo search usually matches across modalities: Text querying is a necessary feature of a photo search engine, but, as the saying goes, a picture is worth a thousand words.
And beyond saving people the effort of so much typing, many visual concepts genuinely defy accurate description. The similarity pivot is a significant addition to the Flickr experience because it offers our community an entirely new way to explore and discover the billions of incredible photos and millions of incredible photographers on Flickr.
And there are many others that you might imagine as well.
What notion of similarity is best suited for a site like Flickr? This requires a deep understanding of image content for which we employ deep neural networks. We have been using deep neural networks at Flickr for a while for various tasks such as object recognition, NSFW prediction, and even prediction of aesthetic quality.
For these tasks, we train a neural network to map the raw pixels of a photo into a set of relevant tags, as illustrated below. Internally, the neural network accomplishes this mapping incrementally by applying a series of transformations to the image, which can be thought of as a vector of numbers corresponding to the pixel intensities.
Each transformation in the series produces another vector, which is in turn the input to the next transformation, until finally we have a vector that we specifically constrain to be a list of probabilities for each class we are trying to recognize in the image.
Instead, we can extract an internal vector in the network before the final output.
But these vectors have an important property: You can think of this as a way that the network has learned to organize information present in the image so that it can output the required class prediction. This is exactly what we are looking for: Euclidian distance in this high-dimensional feature space is a measure of semantic similarity.
The graphic below illustrates this idea: This measure of similarity is not perfect and cannot capture all possible notions of similarity—it will be constrained by the particular task the network was trained to perform, i.
However, it is effective for our purposes, and, importantly, it contains information beyond merely the semantic content of the image, such as appearance, composition, and texture. Most importantly, it gives us a simple algorithm for finding visually similar photos: Of course, there is much more work to do to make this idea work for billions of images.
Additionally, storing a high-dimensional floating point feature vector for each of billions of images takes a large amount of disk space and poses even more difficulty if these features need to be in memory for fast ranking. To understand LOPQ, it is useful to first look at a simple strategy.
Rather than ranking all vectors in the index, we can first filter a set of good candidates and only do expensive distance computations on them. For example, we can use an algorithm like k-means to cluster our index vectors, find the cluster to which each vector is assigned, and index the corresponding cluster id for each vector.
At query time, we find the cluster that the query vector is assigned to and fetch the items that belong to the same cluster from the index.
We can even expand this set if we like by fetching items from the next nearest cluster. This idea will take us far, but not far enough for a billions-scale index.
For example, with 1 billion photos, we need 1 million clusters so that each cluster contains an average of photos. At query time, we will have to compute the distance from the query to each of these 1 million cluster centroids in order to find the nearest clusters.
This is quite a lot. We can do better, however, if we instead split our vectors in half by dimension and cluster each half separately.There are two primary paradigms for the discovery of digital content.
First is the search paradigm, in which the user is actively looking for specific content using search terms and filters (e.g., Google web search, Flickr image search, Yelp restaurant search, etc.).Second is a passive approach, in which the user browses content presented to them (e.g., NYTimes news, Flickr Explore, and.
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