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Unsupervised-learning Methods for Image Clustering

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Presentation on theme: "Unsupervised-learning Methods for Image Clustering"— Presentation transcript:

1 Unsupervised-learning Methods for Image Clustering
Tal Berger and Avishay Shamay Advisors: Oren Freifeld (BGU CS) and Roy Resh (Trax Image Recognition)

2 Project Goal Unsupervised clustering of product images.
Determine the number of cluster automatically.

3 problem Bounding boxes are unavailable
High variability in image quality and viewing angle

4 solution Unsupervised clustering of covariance features
we implemented two algorithms for this propose: K – means GMM EM - Gaussian Mixture Model and Expectation – maximization algorithm

5 Data Representation ( 𝑥 1 , 𝑥 2 ,…, 𝑥 𝑛 )∈ ℝ 𝑛(𝑛+1) 2
First lets talk about what features we chose for each image and how we extract them: y {𝑥,𝑦,𝑟,𝑔,𝑏, 𝜕𝑟 𝜕𝑥 , 𝜕𝑔 𝜕𝑥 , 𝜕𝑏 𝜕𝑥 , 𝜕𝑟 𝜕𝑦 , 𝜕𝑔 𝜕𝑦 , 𝜕𝑏 𝜕𝑦 } 𝑝 11 ⋯ 𝑝 1𝑛 ⋮ ⋱ ⋮ 𝑝 𝑚1 ⋯ 𝑝_𝑚𝑛 Create covariance matrix Using covariance matrix symmetry ⋯ ⋮ ⋱ ⋮ ⋯ ( 𝑥 1 , 𝑥 2 ,…, 𝑥 𝑛 )∈ ℝ 𝑛(𝑛+1) 2 x

6 K - means argmin 𝑆 𝑖=1 𝑘 𝑥∈ 𝑆 𝑖 𝑥− 𝜇 𝑖 2
Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of square. argmin 𝑆 𝑖=1 𝑘 𝑥∈ 𝑆 𝑖 𝑥− 𝜇 𝑖 2

7 GMM - EM expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood estimate from incomplete data. GMM - a probabilistic model for representing the presence of subpopulations within an overall population. Model Selection to find K using BIC: 𝐵𝐼𝐶= ln 𝑛 𝐾−2 ln 𝐿 Bayesian information criterion © C. M. Bishop's book.

8 Conclusion and results
GMM outperforms K-means. 40 < optimal K < 60 Giving higher weights to the color features improved results.

9 Positive results

10 Negative results

11 Q & A


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