The Hotspotter software can be freely downloaded and comes with an additional User Manual. Hotspotter uses a feature-based approach for matching individual images.
The user provides the software with a rectangular region of interest (ROI) and orientation for each individual picture. From this, hotspotter automatically detects elliptical regions centered on points of interest, the so-called hotspots. If two images have enough similarity in hotspots, they are matched by the software. The software ranks all the potential matches, showing for each match a similarity score and highlighting the section of the images it deems the most similar. It is up to the user to select the final match. The type and size of the dataset affects the height of the score, thus the height of the score is only relative to its dataset. After Hotspotter selects an amount of potential matches, it is up to the user to pick the final match.
The mechanism behind Hotspotter consists of two different matching algorithms. The one-vs-one algorithm, matches each image against each database image separately, with a similar mechanism to that of Wild-ID SIFT algorithm. The second algorithm, the one-vs-many algorithm, matches the hotspots from an individual image to all hotspots from the images within the database, using a Local Naive Bayes Nearest Neighbor algorithm. The combined efforts of these algorithms, form the final similarity score (Crall et al, 2016).
As earlier stated, the user needs to provide the region of interest (ROI) and orientation for each individual image. The ROI is the selection of a rectangular area, which must include the distinguishing features of the animal’s body. It is recommended to make the ROI too large instead of too small, as not to cut off any important features.
The orientation of the image is by default horizontal. Although specifying the orientation might not be important for all types of pictures, it is crucial to ensure accurate recognition for overhead pictures. Within the ROI the user draws an axis in a way that can be easily repeated for all images. The developers recommend that for frog images, “the repeatable orientation is selected along the spine, from the tip of the mouth to the tip of the tail”, always in that order.
Related software: IBEIS
The hotspotter software is used as the basis for the matching software of the IBEIS program. The IBEIS program is designed for the storage and management of images, able to compute the species of the animal, detect individual animals and know where an animal is. IBEIS employs algorithms such as “random forest species detection and localization, hessian-affine keypoint detection, SIFT keypoint description, Local Naive Bayes Nearest Neighbor identification using approximate nearest neighbors”. The software is part of the WildBook project. The IBEIS documentation can be found here, yet, if you are lost the author provides some additional guidance within this correspondence.
The IBEIS software is written in Python and can be only operated within a Linux environment, thus it is advised when operating on Windows or OSX I to use a Linux virtual machine. Although based on the Hotspotter software, it is unclear to what extent the matching algorithm of IBEIS is different from its predecessor. However, the IBEIS repository is still being updated, whereas the Hotspotter has not in the last six years.