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A Fast Content-Based Image Retrieval Method Using Deep Visual Features
The document describes a fast content-based image retrieval method using deep visual features, focusing on improving efficiency by utilizing normalized vectors and reducing feature dimensions. Key findings demonstrate that pre-normalizing features significantly speeds up cosine similarity calculations, with at least 8 features deemed necessary for effective image searches. Finally, the author provides instructions for using the implemented method in Elasticsearch and shares their GitHub page for further reference.
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A Fast Content-Based Image Retrieval Method Using Deep Visual Features
- 1. A Fast Content-Based ImageRetrieval Method Using Deep Visual Features Hiroki Tanioka (Assist. Prof. Ph.D.) Tokushima University, Japan
- 2. HELLO! I am HirokiTanioka. You can find me at @taniokah 2
- 3. “Quotations are whywe cannot use cosine similarity on inverted index-based search engine, despite cosine similarity is commonly used for many classification problems.” 3
- 4. Scoring Method Includes normalizingevery dot product score with L2 norm of each indexed document. THERE ARE TWO REASONS FOR THAT Higher Dimension Needs much cost of calculating dot product between query vector and indexed document vector. 4 Calculation Faster Dimension Lower
- 5. Small Ideas My keyconcept is using normalized index and truncating feature vectors. 5
- 6. 1. FASTER COSINE SMILARITY Let’s startmaking index with normalized vectors in advance.
- 7.
- 8. 2. LOWER VECTOR DIMENSTION Let’s reducefeatures un-preferable document features for score.
- 9. SORT AND REDUCE FEATUREVECTOR 9 ・ ・ ・
- 10. Dataset Statistics Number ofImages: 25,000 Type: Dogs and Cats Source: Kaggle Search Engine Elasticsearch 7.0.1 INFROMATION OF EXPERIMENTAL ENVELONMENT Image Features Model: VGG-16 Number of Features: 1,000 Score: Probability Machine Specification MacBook Pro, 2.5 GHz, 16GB (Retina, 15-inch, Mid 2015) 10
- 11. THREE TEST PATERNS FOREVALUATION OF MY SYSTEM Finding a picture with original picture Finding a picture with low-resolution Finding a picture with part of picture 11
- 12. AVERAGE RESPONSE TIME Feature1 2 3 4 5 10 20 30 40 50 100 200 400 1000 dot-prod 0.23 0.33 0.25 0.36 0.37 0.42 0.74 0.69 0.80 0.86 1.43 2.02 2.86 2.87 L1 0.27 0.27 0.30 0.31 0.35 0.35 0.57 0.80 0.70 0.72 1.35 1.58 2.58 2.85 L2 0.26 0.28 0.28 0.30 0.29 0.35 0.51 0.63 0.80 0.99 1.97 3.80 5.78 6.56 cosine 0.23 0.25 0.27 0.28 0.30 0.35 0.64 0.62 0.61 0.59 1.14 1.43 2.60 3.08 dot+cos 44.0 42.2 39.7 36.1 35.0 36.1 35.4 35.0 37.9 41.2 47.0 46.5 52.0 53.0 12 FAST ENOUGH
- 13. 13 Comparison MAP offeature number in 100 top-k results and 1.0 resolution rate. GOOD ENOUGH
- 14. 14 Comparison MAP ofresolution rates, in 1,000 feature number and 100 top-k results. FAIR ENOUGH
- 15. 15 Comparison MAP offeature numbers using quartered partial image query, in 1,000 feature number and 100 top-k results. FAVORABLE
- 16. Pre-Normalizing Is effective forcalculating cosine similarity faster under 100 features. THERE ARE TWO RESULTS Reducing Features Is useful, but at least 8 features are required for our image search experiment. 16
- 17. INSTRUCTIONS FOR USE USEXPACK IN ELASTICSEARCH https://www.elastic.co/blog/text- similarity-search-with-vectors-in- elasticsearch Elasticsearch 7.3 released cosine_similarity in xpack. Still, L1 and L2 are under construction. VISIT TO MY GITHUB PAGE https://github.com/taniokah/icdar- wml-2019 I’ve just already left my messy scripts on Github. I will explain an instruction for my project. 17
- 18. 18 THANKS! Any questions? You canfind me at: ❑ @taniokah ❑ tanioka.hiroki@tokushima-u.ac.jp This work was supported by JSPS KAKENHI Grant Number JP18H03344.