Paper #1 PL@NTNET APP IN THE ERA OF DEEP LEARNING
Key Notes
- Observations are stored within a NoSQL document storage called CouchDb
- CNN architecture is the inception model
- The number of species (i.e. classes) in January 2017 was about 10K and the number of training images about 332K
- Species filtering is applied based on the checklist of species
- activated within the app (e.g. West Europe, North Africa, South America, etc.)
- By default, this checklist is automatically chosen according to the geo-location of the mobile device, but the user also has the possibility to select another one manually
- Similarity Search - This content-based image retrieval is performed through a hashing-based approximate nearest neighbors search algorithm applied on top of the 1024-dimensional feature vectors extracted by the last hidden layer of the fine-tuned CNN
Paper #2 - Fine-grained recognition of plants from images
Key Notes
- A number of approaches is based on the popular local binary patterns (LBP)
- Fast Features Invariant to Rotation and Scale of Texture (Ffrst)
- "One versus All" classifcation scheme is used for multi-class classifcation
- The Foliage leaf dataset by Kadir
- The Swedish leaf dataset
- The Leafsnap dataset
Paper #3 - Neural Network Application on Foliage Plant Identification
Key Notes
- Polar Fourier Transform that proposed by Zhang [18] has properties that are very useful for represents shape of objects, including leaf of plants
- There are three kinds of geometric features involved as shapes features: slimness ratio, roundness ratio, and dispersion.
- Color features on a leaf can be extracted by using statistical calculations such as mean, standard deviation, skewness, and kurtosis
Paper #4 - An Online Algorithm for Large Scale Image Similarity Learning
Key Notes
- Here we focus on a weaker supervision signal: the relative similarity of different pairs
- we extract similarity information from pairs of images that share a common label
- we extract similarity information from pairs of images that share a common label or are retrieved in response to a common text query in an image search engine
Paper #5 - Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive
Paper #6 - Comparison of Image Matching Techniques
- Blob detection technique
- Template matching
- SURF feature extraction
Paper #7 - Large Scale Online Learning of Image Similarity Through Ranking
Key Notes
- OASIS is both fast and accurate at a wide range of scales
- Similarity information is extracted from pairs of images that share a common label or are retrieved in response to a common text query
- OASIS can be trained on more than two million images within three days on a single CPU
- OASIS learned similarity show that 35% of the ten nearest neighbors of a given image are semantically relevant to that image
- Algorithm that uses triplets of images
- Color histograms are obtained by K-means clustering
- Local Binary Pattern
- LMNN - Large Margin Nearest Neighbor Classification
Paper #8 - Sparse online learning of image similarity
tf2_semantic_approximate_nearest_neighbors
More Reads
- Simple and fast method to compare images for similarity
- Sampling Wisely: Deep Image Embedding by Top-k Precision Optimization
- image-match
- Image Deduplicator (imagededup)
- Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive
- Image similarity model
- Hashing Learning
- Fast Near-Duplicate Image Search using Locality Sensitive Hashing
- Querying Similar Images with TensorFlow
- Image classification with Keras and deep learning
- K-Nearest Neighbors Hashing
- A Survey on Deep Hashing Methods
- Image similarity estimation using a Siamese Network with a triplet loss
- Finding similar images using Deep learning and Locality Sensitive Hashing
- Fingerprinting Images for Near-Duplicate Detection
- LSH for near-duplicate image detection
Keep Exploring!!!
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