Semantic Image Search. Alex Egg
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1 Semantic Image Search Alex Egg
2 Inspiration Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems
3 AlexNet 2012 Deep CNN Classifier Feature Generator C1 C2 C3 C4 C5 FC6 FC7 FC8 c) First Pooling Layer d) second to last FC Layer Decaf
4 Nearest Neighbor Search Images in 2D space
5 Semantic Search Reverse Image Search: Image A -> Identical image of A Semantic Image Search: Image A -> Any images containing A The word "semantic" refers to the meaning or essence of something Text Semantics: Sentiment & Meaning Image Semantics: Object Quantification
6 Theoretical Implementation Setup DCNN (Image Feature Generator) Setup Database Index feature vectors in database Query database using 1-Nearest Neighbor Search
7
8 Theoretical Impl. Problems Naive Solution Accuracy: Distance measure breakdown in high dimensions Scalability: Storage & Nearest Neighbor intractability
9 Problem: Accuracy AlexNet: 4096D VGG: 4096D Inception V3: 2048D Inception V4: 1536D After about 10D all points are equally far away -- distance measure break down: Curse of Dimensionality
10 Problem: Scalability Storage Complexity: 4096x32 bits float = KB/vector x 1M images = GB Computational Complexity: 1NN = O(n); 1ms*1e6=16.6m We can mitigate most of these problems using a modern database system.
11 Solutions Scalability: Reduce search space Accuracy: Reduce Dimensionality
12 Dimensionality Reduction PCA: Directions of projection are data-dependent Random Projections: Direction of projections are data-independent Data is so high dimensional that it is too expensive to compute PCA You don t have access to the data all at once, as in streaming
13
14 /\ 2
15 /\ /\/\ 4
16 /\ /\/\ /\/\ 6
17 /\ /\/\ /\/\ /\/\ 8
18 /\ /\/\ /\/\ /\/\ 8
19 /\ /\/\ /\/\ /\/\ 8
20 Johnson-Lindenstrauss Lemma The Johnson-Lindenstrauss Lemma: A set of p points in high-dimensional space can be linearly embedded in m > (12 log p) dimensions without distorting the distance between any two points by more than a factor of (1 ± ε). m > (4 log 1e6) m > 55 2^5 = 32 splits 2^6 = 64 splits
21 Hash Table Binary Tree Search is O(log n) Hash Table lookup is O(1) Bad Hashing Function (Maximizes Collisions) CNN Image Vector 4096D Hashing Function: h(v) = sgn(v r), that is h(v) = ±1 depending on what side of the hyperplane v lies. 4 Random Projections
22 Locality Sensitivity Hashing (LSH) Keep splitting until node are small enough Median splits give nicely balances trees Build a forest
23
24 /\ 2
25 /\ /\/\ 4
26 /\ /\/\ /\/\ 6
27 /\ /\/\ /\/\ /\/\ 8
28 Forrest
29 Smart Implementation Same DCNN Feature Extractor Database to store hashtable instead of vectors Index Features in Database Approximate Nearest Neighbors using LSH
30 Query: Giraffe & Zebra Results: Giraffes and/or Zebras in various colors, varieties & orientations
31 Query: Human Face Results: Human Faces in various colors, varieties & orientations
32 Query: Cat Results: Cats in various colors, varieties & orientations
33 Query: Polar Bear Results: Polar bears in various orientations + white sheep
34 Query: Grizzly Bear Results: Bears in various colors, varieties & orientations
35 Query: Orange Cat Results: Cats in various colors, varieties & orientations
36 Query: Giraffe Results: Giraffes various colors, varieties & orientations
37 Future Work Text -> Image Search: Type in text phrase, then convert into a point in the same high-dimensional space as the images Deployment w/ kubernetes
38 Appendix
39 History 2005: No source control! 2010: Source control & continuous builds, yay!.. but not for ML :( 2017: Great tools!.. still have a way to go for ML.
40 Distance Measures Distance between two points in N dimensional space: Euclidian Cosine Manhattan
41 Naive Implementation Feature Generator: TensorFlow Serving VGG16/FC6 Database: PostgreSQL Frontend: Flask Deployment: Docker & Kubernetes
42 VGG16 Tensorflow Implementation 103 images/s on CPU 711 images/s on Tesla V100 GPU
43 Reverse image search in PostgreSQL using vector operations: rows are CNN image vectors. Euclidian Distance exhaustive search against query image. This is as opposed to the naive solution of doing exhaustive euclidian search in memory.
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ISSN 2320-9194 1 Volume 5, Issue 4, April 2017, Online: ISSN 2320-9194 RETRIEVAL OF FACES BASED ON SIMILARITIES Jonnadula Narasimha Rao, Keerthi Krishna Sai Viswanadh, Namani Sandeep, Allanki Upasana ABSTRACT