Paper - Class-Balanced Loss Based on Effective Number of Samples

• In general, there are two strategies: re-sampling and cost-sensitive re-weighting
• In re-sampling, the number of examples is directly adjusted by over-sampling
• In cost-sensitive re-weighting, we influence the loss function by assigning relatively higher costs to examples from minor classes softmax cross-entropy, sigmoid cross-entropy and focal loss.

Code - Link

Keep Exploring!!!

Leaf Classification

Leaf Classification

Paper #1 - Plant identification using deep neural networks via optimization of transfer learning parameters

Key Notes

• 1.2 million labeled images of 1,000 different categories from the ImageNet = one thousand two hundred per class
• LifeCLEF 2015 - 91,758 labeled images of different plant organs (e.g. flowers, fruits, leaves, and stems), from 1,000 - 91 per class

Parts of Plant

• Branch 
• Entire 
• Flower 
• Fruit 
• Leaf 
• LeafScan 
• Stem 
• Overall

• Increasing the batch size from 20 to 60 improves the overall accuracy
• 80 patches for data augmentation

Paper #2 - Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

Key Notes

• Feature engineering approaches such as Scale-invariant
• feature transform (SIFT), Bag of Word (Bow), Speeded-Up
• Robust Features (SURF), Gabor, Local Binary Pattern (LBP).
• Most generally used features to distinguish leaves of different species
• Hybrid generic-organ convolutional neural network, abbreviated HGO-CNN
• Three different sizes: 256, 384 and 512
• Crop 256 × 256 center pixels
• Multi-Scale Plant Images Generation
• During network training, 224 × 224 pixels are randomly cropped from the rescaled images and fed into the network

Keep Exploring!!!

Leaf Detection - Top K Techniques - Reads

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

Leaf Type Classification

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!!!

Leaf / Plant Detection - Reads :)

Never stop collecting / identifying/analyzing perspectives :).

Paper #1 - Real-world plant species identification based on deep convolutional neural networks and visual attention

Key Notes

• Data augmentation method for deep learning
• Crop the image in terms with visual attention
• Considering flowers and fruits of plants are seasonal, some researchers believe that leaves are more suitable for identification
• Samples is also strict

• PlantCLEF. There are different view types and the samples are close to realistic scenarios.

• Image segmentation is carried out for generating the regions of interest (ROI) for recognition
• Comparisons between original images and final attention cropping results.

Paper #2 - Deep Learning in Agriculture: A Survey

Key Notes

• High occlusion, depth variation, and uncontrolled illumination, including high color similarity between fruit/foliage
• Rotations, cropping, scaling, transposing, mirroring
• DetectNet CNN
• Faster Region-based CNN, DetectNet CNN

Paper #3 - PlantDoc: A Dataset for Visual Plant Disease Detection

Key Notes

• The PlantVillage dataset contains images taken under controlled settings. 
• Final dataset having a total of 27 classes spanning over 13 species with 2,598 images
• model which can detect a leaf in an image and then classify it into the particular classes

Paper #4 - Leaf Classification Using Shape, Color, and Texture Features

Key Notes

• Texture, on its own does not have the capability of finding similar images, but it can be used to classify textured images from non-textured ones 
• Texture features can be extracted by using various methods. Gray-level occurrence matrices (GLCMs), Gabor Filter, and Local binary pattern (LBP) 

• Fourier descriptors, slimness ratio, roundness ratio, and dispersion are used to represent shape features

Paper #5 - Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

Key Notes

• Feature engineering approaches such as Scale-invariant feature transform (SIFT), Bag of Word (Bow), Speeded-Up Robust Features (SURF), Gabor, Local Binary Pattern (LBP)

Take multiple images and deduce plant, flower, leaf

Paper #6 - Two-View Fine-grained Classification of Plant Species

• Three levels of abstraction: family, genus, and species
• The input of the SCNN model is the whole leaf image characterizing a global
• view in terms of problem representation. The output of this stage is a ranked list of the top-K genus candidates
• SCNN takes into account global features extracted from the entire leaf image (shape and color), while in the second view, local features based on texture and the plant veins are considered. 
• The output of the first stage is a ranked list of the top-k genus candidates.
• In the second stage, given the top-k genus candidates found in the first stage, a fine classification considering only the plant species which belong to such a genus is performed

• A coarse-to-fine classification is performed considering the hierarchical botanic taxonomy
• e. Finally, the genus (coarse classification) and species (fine classification) are combined to produce a final ranked list of the k-best hypotheses of plant species.

LifeCLEF 2015 and LeafSnap datasets.

Paper #7 - Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Key Notes

• We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers
• Object detection has mostly had its use in plant analysis constrained to invasive species detection
• First, an object detector identifies and localizes plant organs, including leaves, flowers, fruit, stems, and regions with a high volume of leaves, termed “highdensity leaves” (HDL)
• These regions of interest (ROIs) are then individually passed into an organ-based species classifier
• Leaf, flower, fruit, stem, HDL

Paper #8 - COMPUTER-AIDED INTERPRETABLE FEATURES FOR LEAF IMAGE CLASSIFICATION

Key Notes

• i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove the stalk, vi) Closing holes, and vii) Resize the image.

• Gray-scaling is the process of converting an image to shades of gray from other colour spaces like RGB
• Image smoothing techniques help in smoothing images and removing intensity edges.
• Thresholding is a segmentation technique that is used to separate the foreground from its background

Paper #9 - A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

• Leaf Snap Dataset
• The Middle European Wood Database
• The Smithsonian Leaf Dataset
• The Austrian Federal Forest (AFF) Dataset
• Pl@ntNet

More Reads

• Flower Categorization using Deep Convolutional Neural Networks
• A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
• Large-Scale Plant Classification with Deep Neural Networks

Keep Exploring!!!