Manta Matcher: automated photographic identification of manta rays using keypoint features

Abstract

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern-matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale-Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern-matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life-history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.

Sloop: A pattern retrieval engine for individual animal identification

“…Although all techniques benefit from relevance feedback, the primary observation of this experiment, we also observe that SIFT performs best overall, a conclusion shared by Town et al [17] for manta rays. There is a large body of vision work engaged in the Fig.…”

“…This is suggested by several authors [19,26,17] as a way to gather images. The conceptualization here is different.…”

“…SIFT features [20] are popular generic features. For example, Yang and Ravela [9] apply it to marbled salamanders showing performance similar to multiscale-PCA, and Town et al [17] use SIFT features to characterize the ventral surface area of manta rays. They compare different features, including SURF [21] and ORB [22], arguing that SIFT works well on small collections.…”

“…Within the realm of feature-based methods, there are two main classes of recognition methods; specialized methods [6,15,16] for individual species, and generic methods [8,1,9,[17][18][19]10]. The specialized techniques are of limited interest.…”

Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures

“…SIFT features share similar properties with neurons in the primate inferior temporal cortex 26,29 and likely correspond to features important for object recognition in other vertebrates, including birds 28 . SIFT has revolutionized computer-assisted recognition tasks in the field of computer vision and has recently been used to recognize handwriting 46 , detect forged digital images 47 and identify individual wild animals based on their markings 48,49 . SIFT's usefulness to evolutionary biologists has so far been restricted to biometric identification 48 , but we believe it can be powerfully implemented in research on animal signalling, recognition and communication as well as pattern formation and development.…”