"Leveraging Computer Vision and Machine Learning to Power the Visual Commerce Revolution," a Presentation from Sight Commerce

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For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sight-commerce For more information about embedded vision, please visit: http://www.embedded-vision.com Satya Mallick, Co-Founder of Sight Commerce, delivers the presentation "Leveraging Computer Vision and Machine Learning to Power the Visual Commerce Revolution" at the May 2015 Embedded Vision Alliance Member Meeting. Satya explains how his company is using vision to enable retailers like Bloomingdale’s to create more engaging, personalized shopping experiences.

Technology

Transforming
Beauty & Fashion
using Computer Vision & Machine Learning
Satya Mallick, Ph.D.
Co-Founder, Sight Commerce

Nothing is as powerful as an idea
whose time has come

Vision Graphics Learning
Beauty & Fashion
DISRUPT

Why?
Pour quoi?
Por quê?
Zergatik?
Per ché?
Warum?
क्यों ?
Почему?

Beauty & Fashion
Shopping is Visually Inspired

Intel
• Speed : 3.50 GHz
• Cache : 20MB
• Number of Cores : 4
• Instruction Set : 64-bit
• Power : 140 W

Intel
• Speed : 3.50 GHz
• Cache : 20MB
• Number of Cores : 4
• Instruction Set : 64-bit
• Power : 140 W
Victoria’s
Secret

Communication is Visual
1.8B Photos / Day

Extreme
Personalization
of the shopping experience is
Inevitable

Extreme
Personalization
of the shopping experience is
Inevitable
Image based
Recommendations

CVML @Sight Commerce
• Virtual Makeover
• Jewelry try-on
• Virtual Nails
• Virtual Clothing for ecommerce
• Product recommendation based on facial analysis
• Apparel recommendation based on photo

• Lipstick
• Lipgloss
• Lipliner
• Blush
• Bronzer
• Foundation
• Concealer
• Eyeliner
• Macara
• Eyeshadow
• Contacts
Virtual Makeover

User Uploads a Photo
Facial Feature Detection
1. Active Appearance Model
2. Supervised Descent Method
3. Deep Learning based

User Uploads a Photo
Facial Feature Detection
1. Active Appearance Model
2. Supervised Descent Method
3. Deep Learning based
Head Pose Estimation
Facial features used to estimate pose relative to
a canonical 3D shape

10 models x 100 lip products x 100 eye products x 10 blush

1,000,000
10 models x 100 lip products x 100 eye products x 10 blush

Image Based
Recommendation
“35 percent of product sales result from recommendations”
— Amazon

Photo Analysis + Shopping Data
=
Extremely Personalized
Recommendation

User Photo Analysis
• Skin Tone
• Eye Color
• Lip Color
• Hair Color
• Ethnicity
• Face Shape
• Wearing Glasses ?

Color & Pattern based
Apparel Recommendation

Wish List
Face Processor
Landmark detector
Skin detection
Color Measurement
Inexpensive
Pattern Classiﬁcation

To develop a scalable face image restitution system we apply Attribute-Enhanced Sparse Code words on local features extracted from face images combining with inverted indexing to construct an efficient and scalable face retrieval system. Nowadays Photos with people are the major interest of users. Among all those photos, a big percentage of them are photos with human faces. The importance and the sheer amount of human face photos make manipulations (e.g., search and mining) of large-scale human face images a really important research problem and enable many real world applications. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. To utilize automatically detected human attributes that contain semantic cues of the face photos to improve content based face retrieval by constructing semantic code words for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attribute embedded inverted indexing to improve the face retrieval in the offline database. We investigate the effectiveness of different attributes and vital factors essential for face retrieval.

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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

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Clustering Based Approximation in Facial Image Retrieval

IJERA Editor

The web search tool returns a great many pictures positioned by the essential words separated from the encompassing content. Existing article acknowledgment systems to prepare characterization models from human-named preparing pictures or endeavor to deduce the connection/probabilities in the middle of pictures and commented magic words. Albeit proficient in supporting in mining comparatively looking facial picture results utilizing feebly named ones, the learning phase of above bunch based close estimations is shortened with idleness elements for ongoing usage which is fundamentally highlighted in our showings. So we propose to utilize shading based division driven auto face location methodology combined with an adjusted Clustering Based Approximation (CBA) plan to decrease the dormancy but then holding same proficiency amid questioning. The specialized phases of our proposed drew closer is highlighted in the accompanying stream diagram. Every phase of the above specialized procedure guarantees the question results at tremendously lessened handling time in this way making our method much achievable for ongoing usage.

Region based elimination of noise pixels towards optimized classifier models ...

IJERA Editor

The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.

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“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/bias-in-computer-vision-its-bigger-than-facial-recognition-a-presentation-from-santa-clara-university/ Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit. As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias. This talk explores how bias in computer vision often poses an ethical challenge, regardless of the stakes involved. Kennedy discusses the limitations of technical solutions aimed at mitigating bias, and why “bias-free” AI may not be achievable. Instead, she focuses on the importance of adopting a “bias-aware” approach to responsible AI design and explores strategies that can be employed to achieve this.

“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/sensor-fusion-techniques-for-accurate-perception-of-objects-in-the-environment-a-presentation-from-sanborn-map-company/ Baharak Soltanian, Vice President of Research and Development for the Sanborn Map Company, presents the “Sensor Fusion Techniques for Accurate Perception of Objects in the Environment” tutorial at the May 2023 Embedded Vision Summit. Increasingly, perceptual AI is being used to enable devices and systems to obtain accurate estimates of object locations, speeds and trajectories. In demanding applications, this is often best done using a heterogeneous combination of sensors (e.g., vision, radar, LiDAR). In this talk, Soltanian introduces techniques for combining data from multiple sensors to obtain accurate information about objects in the environment. Soltanian briefly introduces the roles played by Kalman filters, particle filters, Bayesian networks and neural networks in this type of fusion. She then examines alternative fusion architectures, such as centralized and decentralized approaches, to better understand the trade-offs associated with different approaches to sensor fusion as used to enhance the ability of machines to understand their environment.

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