_Objective:_ Analyze large scale dataset of fashion images to discover visually consistent style clusters.

*   _Dataset:_ StreetStye-27K.
*   _Code:_ demo [here](http://streetstyle.cs.cornell.edu/)

## New dataset: StreetStye-27K

1.  **Photos (100 million)**: from Instagram using the [API](https://www.instagram.com/developer/) to retrieve images with the correct location and time.
2.  **People (14.5 million)**: they run two algorithms to normalize the body position in the image:
    *   [Face++](http://www.faceplusplus.com/) to detect and localize faces.
    *   [Deformable Part Model](http://people.cs.uchicago.edu/%7Erbg/latent-release5/) to estimate the visibility of the rest of the body.
3.  **Clothing annotations (27K)**: Amazon Mechanical Turk with quality control. 4000$ for the whole dataset.

## Architecture:

Usual GoogLeNet but they use [Isotonice Regression](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) to correct the bias.

## Unsupervised clustering:

They proceed as follow:

1.  Compute the features embedding for a subset of the overall dataset selected to represent location and time.
2.  Apply L2 normalization.
3.  Use PCA to find the vector representing 90% of the variance (165 here).
4.  Cluster them using a [GMM](https://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model) with 400 mixtures which represent the clusters.

They compute fashion clusters for city or bigger entities:  
[![screen shot 2017-06-15 at 12 04 06 pm](https://user-images.githubusercontent.com/17261080/27176447-d33fc2dc-51c2-11e7-9191-dbf972ee96a1.png)](https://user-images.githubusercontent.com/17261080/27176447-d33fc2dc-51c2-11e7-9191-dbf972ee96a1.png)

## Results:

Pretty standard techniques but all patched together to produce interesting visualizations.