Cognitive Fashion Data is the new black Vikas Raykar IBM Research
Fashion Zeitgeist What is the world wearing right now? Fashion designers usually start conceptualizing and designing products for the new season six months to one year prior to the actual selling season. In recent times this has been drastically reduced with the emergence of fast-fashion retailers.
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Therefore, for the fashion industry, knowing what the customers would like to wear next season or right now is extremely important. A crucial aspect to achieve this is to have the ability of analyze current fashion trends and forecast future fashion trends.
What is a trend ? A trend refers to a general direction (typically upward) in which something is developing or changing. A trend can arise from a multitude of sources. For example, there is a trend toward animal prints, could mean that high-end fashion designers have started showing animal print designs on the runway apparel retailers have started to introduce them and are quickly selling out celebrities have been spotted sporting animal prints fashion magazines, websites, blogs and social media sites have started recognizing this trend and many fashion-forward consumers have starting wearing animal prints on the street.
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Fashion runway trends Years : 2013-2017 Seasons : Autumn-Winter/Spring-Summer Show type : Ready-to-wear/Couture Locations : New York/London/Paris/Milan Around 200 thousand images are available for the for the past 5 years from the big four fashion runway shows. 202000 images 5821 runway shows 1703 designers http://www.vogue.co.uk/shows
Data Deluge season
events
location
events
show type
events
Autumn/Winter 2013
547
New York
1646
Ready-To-Wear
5612
Spring/Summer 2013
473
London
1846
Couture
209
Autumn/Winter 2014
534
Paris
1226
Spring/Summer 2014
515
Milan
507
Autumn/Winter 2015
613
Vancouver
232
Spring/Summer 2015
583
Copenhagen
95
Others
269
Autumn/Winter 2016 670 Spring/Summer 2016 566 Autumn/Winter 2017 618 Spring/Summer 2017 702
Fashion Zeitgeist A cognitive system that can analyze current fashion trends from multiple sources (catalogs, articles, blogs, images, social media) and forecast future fashion trends.
We have broken down this into three phases 1. Color 2. Print 3. Silhouette/Style
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Cognitive Fashion We are developing capabilities that can understand a fashion article or a fashion image. We use these capabilities to analyze current fashion trends from multiple sources (catalogs, articles, blogs, images, social media) and forecast future fashion trends.
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COLOR TRENDS The story of fashion is the story of color. 1. 2. 3.
dominant color palette color trend analysis color trend prediction
Face Detection Watson Visual Recognition is used to detect the face in the image. Pose Detector This is a IBM Research cognitive fashion asset that determines the model pose. Body and Apparel Detectors Based on the detected face and pose we detect the body and apparel. Dominant Color Palette This is a IBM Research cognitive fashion asset that determines the dominant colors in a fashion image.
Visual Recognition understands the contents of images - visual concepts tag the image, find human faces, approximate age and gender, and find similar images in a collection.
IBM Research Cognitive Fashion is a suite of APIs, assets, and use cases tuned for the fashion retail industry.
Dominant Color Palette
methods
Dominant Color Palette most popular colors for each season
For each runway image we extract the dominant colors for the apparel. We then analyze the entire collection of fashion images for each season to get the dominant color palette.
Dominant Color Palette
Data is the new black
Dominant color for 2012 autumn winter season.
Analyze a collection of fashion images (e.g. from runway shows and social media) to predict (and visualize) the dominant color palette for the apparel.
demo
Dominant color palette AW 2017 The panel shows 12 dominant colors for each season. The color is represented by the RGB value and we also indicate the closest color name. This panel below shows a sample image from a designer for each dominant color.
Dominant color palette AW 2016 The panel shows 12 dominant colors for each season. The color is represented by the RGB value and we also indicate the closest color name. This panel below shows a sample image from a designer for each dominant color.
Dominant color palette SS 2017 The panel shows 12 dominant colors for each season. The color is represented by the RGB value and we also indicate the closest color name. This panel below shows a sample image from a designer for each dominant color.
Dominant color palette SS 2016 The panel shows 12 dominant colors for each season. The color is represented by the RGB value and we also indicate the closest color name. This panel below shows a sample image from a designer for each dominant color.
Color Trend analysis popular colors This reflects how popular the color was for that season. trending colors This reflects the color which had the maximum increase in popularity over the past 3 seasons. These are the colors which may not be that popular but there is sharp increase in its popularity compared to the last few seasons.
Color Predictions We analyze the dominant colors for each season for the past 5 years and predict the popular and trending colors for the next season. popular colors This reflects how popular the color was for that season. trending colors This reflects the color which had the maximum increase in popularity over the past 3 seasons. These are the colors which may not be that popular but there is sharp increase in its popularity compared to the last few seasons.
Predictions AW 2018 popular colors - This reflects how popular the color will be for that season. trending colors - This reflects the color which will have the maximum increase in popularity over the past 3 seasons.
Predictions SS 2018 popular colors - This reflects how popular the color will be for that season. trending colors - This reflects the color which will have the maximum increase in popularity over the past 3 seasons.
Predictions AW 2018 – finer granularity* popular colors - This reflects how popular the color will be for that season. trending colors - This reflects the color which will have the maximum increase in popularity over the past 3 seasons. *We have a parameter in our analysis that controls at what level the colors have to be quantized. For these we repeat the same analysis but change the color quantization to a finer granularity.
Predictions SS 2018 – finer granularity popular colors - This reflects how popular the color will be for that season. trending colors - This reflects the color which will have the maximum increase in popularity over the past 3 seasons.
Cognitive Couture Turning cold data into warm fabric
color trend prediction popular coffee
spicy mix
dark brown tangelo
111-78-55
139-95-77
136-101-78
36-33-36
old lace
naples yellow
earth yellow
sinopia
unbleached silk
persimmon
gargoyle gas
253-245-230
250-218-94
225-169-95
203-65-11
255-221-202
236-88-0
255-223-70
old lace
253-245-230
trending
raisin black
dark vanilla
beaver
pale silver
spanish gray
209-190-168
159-129-112
201-192-187
152-152-152
188-152-126
teal deer
maximum blue purple
princeton orange
155-230-179
172-172-230
245-128-37
pale taupe
“I've chosen these as the work really beautiful together and I can mentally visualize the collection on the runway. They are soft, luxurious and fresh.� - - Jason Grech 28
color trend prediction popular coffee
spicy mix
dark brown tangelo
111-78-55
139-95-77
136-101-78
36-33-36
old lace
naples yellow
earth yellow
sinopia
unbleached silk
persimmon
gargoyle gas
253-245-230
250-218-94
225-169-95
203-65-11
255-221-202
236-88-0
255-223-70
old lace
253-245-230
trending
raisin black
dark vanilla
beaver
pale silver
spanish gray
209-190-168
159-129-112
201-192-187
152-152-152
188-152-126
teal deer
maximum blue purple
princeton orange
155-230-179
172-172-230
245-128-37
pale taupe
“I've chosen these as the work really beautiful together and I can mentally visualize the collection on the runway. They are soft, luxurious and fresh.� - - Jason Grech 29
STRUCTURES
IBM CONFIDENTIAL
IBM CONFIDENTIAL
IBM CONFIDENTIAL
Visual Discovery The visual discovery tool will allow you to explore all the fashion runway images interactively. We have ingested close to 500 thousand runway images for a decade (from 2006 to 2016) for both Spring/Summer and Autumn/Winter collections from various designers. Visual Search You can upload any fashion image of your liking and the tool will show similar looks from various runway shows. Visual Browse You can also click on any existing image and it shows other similar looks. This is based on the IBM Research cognitive fashion asset that determines the similarity between any two fashion images. The notion of similarity can cover various aspects like color, pattern, cut, silhouette etc. This tool is also useful to see how similar looks evolve over time and how fashion designers get influenced by each other.
http://visualdiscovery.mybluemix.net/
Design inspiration You can also upload non-fashion images (for example, architecture details, art, etc.) and see similar looking fashion images to get some ideas on how to translate inspiration from other areas into fashion.
visual search You can upload any fashion image of your liking and the IBM Visual Fashion Search cognitive agent will show similar looks from various runway shows from the vogue archive. This tool is useful to see how similar looks evolve over time and how fashion designers get influenced by each other.
visual browse You can also click on any image and it will show similar looks from various runway shows from the vogue archive.
fashion inspired by architecture This is an experimental feature but you can actually upload any image, for example, architecture close ups and the app will bring up visually similar images.
Jason is already using it to develop 2 dresses this week. He noted that the visual discovery tool is super easy to use and that he has actually also been uploading images of silhouettes not just architecture and it has been giving him dresses in the same shape which has been really helpful also.
IBM CONFIDENTIAL
Decoding Australian street fashion Fashion is not just about runway shows or fashion magazines, it is also about what people are wearing on the street. It provides a real-life and real-time snapshot into what’s popular and what consumers think is trending. During this engagement we analyzed public images from the street and social networks during the Melbourne Spring Fashion Week 2016 to understand the dominant color trends. This data was then turned into twitter posts to provide a zeitgeist barometer of the fashion sentiment on the city’s streets.
Color trends discovered across different streets The week prior to and during fashion week itself, cameras were placed at strategic points around the city. Triggered by sensors, the cameras would capture pictures of passers-by which were then analyzed for the color trends.
Color trends discovered across different states These are based on public fashion images posted on social networks.
Fashion Zeitgeist A cognitive system that can analyze current fashion trends from multiple sources (catalogs, articles, blogs, images, social media) and forecast future fashion trends.
We have broken down this into three phases 1. Color 2. Print 3. Silhouette/Style
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Bollywood data We also analyzed images from various Bollywood celebrities from Instagram and produced the dominant color palette.
Dominant color palette – Bollywood celebrities The panel shows 12 dominant colors. The color is represented by the RGB value and we also indicate the closest color name. This panel below shows a sample image for each dominant color.
Dominant Print Palette most popular prints for each season For each runway image we extract the dominant print swatch. We then analyze the entire collection of fashion images for each season to get the dominant print palette.
dominant prints dominant prints
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Dominant print palette AW 2017 The panel shows some dominant prints for each season.
Dominant print palette AW 2017 This panel below shows a sample image from a designer for each dominant print.
Dominant print palette AW 2016 The panel shows some dominant prints for each season.
Dominant print palette AW 2016 This panel below shows a sample image from a designer for each dominant print.
Dominant print palette SS 2017 The panel shows some dominant prints for each season.
Dominant print palette SS 2017 This panel below shows a sample image from a designer for each dominant print.
Dominant print palette SS 2016 The panel shows some dominant prints for each season.
Dominant print palette SS 2016 This panel below shows a sample image from a designer for each dominant print.
Dominant Silhouettes For each season we cluster all the images to visualize the dominant silhouettes for that season. (see the next few slides) The key silhouette is an exemplar for that cluster. You can further explore other visually similar looks for each of the dominant silhouette.
http://silhouette.mybluemix.net/
SS 2017 The key silhouette is an exemplar for that cluster. You can further explore other visually similar looks for each of the dominant silhouette.
AW 2017 The key silhouette is an exemplar for that cluster. You can further explore other visually similar looks for each of the dominant silhouette.
SS 2016 The key silhouette is an exemplar for that cluster. You can further explore other visually similar looks for each of the dominant silhouette.
AW 2016 The key silhouette is an exemplar for that cluster. You can further explore other visually similar looks for each of the dominant silhouette.
Average Silhouettes AW 2017 For each season we cluster all the images to visualize the dominant silhouettes for that season. The average silhouette is the average look for all images for that cluster.
Average Silhouettes SS 2017 For each season we cluster all the images to visualize the dominant silhouettes for that season. The average silhouette is the average look for all images for that cluster.
Visual Discovery The visual discovery tool will allow you to explore all the fashion runway images interactively. We have ingested close to 500 thousand runway images for a decade (from 2006 to 2016) for both Spring/Summer and Autumn/Winter collections from various designers. Visual Search You can upload any fashion image of your liking and the tool will show similar looks from various runway shows. Visual Browse You can also click on any existing image and it shows other similar looks. This is based on the IBM Research cognitive fashion asset that determines the similarity between any two fashion images. The notion of similarity can cover various aspects like color, pattern, cut, silhouette etc. This tool is also useful to see how similar looks evolve over time and how fashion designers get influenced by each other.
http://visualdiscovery.mybluemix.net/
Decoding Street Fashion Fashion is not just about runway shows or fashion magazines, it is also about what people are wearing on the street. Fashion enthusiasts connect with each other on by posting OOTD on social media.
Method Parse the text of the post to identify the color pattern and apparel which appear together Perform frequent pattern mining on these parsed text to obtain street fashion rules of colors, patterns and apparels which appear together.
Social Signal Analyzer
We use the social signal and the frequency of cooccurrence to normalize the score of the rules
Text Parser
Popular color trends in women street fashion
black and olive green score: 0.98
black and ruby red score: 0.98
tan and navy blue score: 0.94
bubble gum and silver score: 0.92
hot pink and white score: 0.93
Popular color trends in men street fashion
black and heather grey score: 0.98
brown and silver score: 0.90
brown and navy blue score: 0.96
black and brown score: 0.95
navy blue and white score: 0.96
Popular trend in women street fashion blue distressed jeans and black top score: 0.97
Popular color and apparel trend in men street fashion white woven shirt and heather grey coat score: 0.93
Cognitive Prints Can a computer be creative ? An AI powered system that can analyze a large collection of print swatches and generate its own novel prints never seen earlier.
These are based on recent advances in generative adversarial neural networks. The system analyzes a large collection of images and tries to develop an understanding of these set of images. Once the model is trained we can ask the system to generate new prints on it own which are not in the data it has seen so far. For this app we trained the model on close to 100 thousand fabric print swatches.
http://watsoncognitiveprints.mybluemix.net
Cognitive Prints We use the images shared by FSP as inspiration to generate novel prints. We use the state-of-the-art ]deep neural nets which generates multiple samples of arbitrary size for a given an example texture. To this end, it starts the process of generation with a randomly generated noisy texture and tries to match the features of this noisy texture with the example texture. From the set of generated textures, we pick the one which is visually appealing.
http://wwd.com/business-news/technology/ibm-watson-fashion-week-analysis-10842213/
designer coach 53 jonathan simkhai 45 delpozo 42
marc jacobs 41 alexander wang 39 brandon maxwell gg 39 plim 39 public school 38 ralph lauren 36 prabal gurung 34 jason wu 31 dion lee 30
data analyzed • 467 images • 12 designers • NYFW 2017
designer influences and similarities most popular colors We want to analyze the similarities between various designers and how they are influenced by each other. We computed similarity score for each pair of designers, by aggregating the similarity between their image collections. We plot a graph where nodes are the designers and edge length represent the similarity scores between two designers. That is, shorter the edge, more similar are the two designers.
Illustration Alexander Wang and Brandon Maxwell are the closest and have strong similarity. This garment by Alexander Wang is similar to multiple designs by Brandon Maxwell in terms of color, cuts and knee length.
Illustration Prabal Gurung designs are highly similar to Jonathan Simkhai’s color and patterns.
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