Marine Animal Classification with Correntropy Loss Based Multi-view Learning

TL;DR

This paper introduces a robust multi-view learning approach using correntropy loss for classifying marine animals from imagery, effectively handling noise and improving accuracy.

Contribution

It develops a novel multi-view learning framework with correntropy loss for feature and dissimilarity matrix data, enhancing noise robustness in marine animal classification.

Findings

Improved classification accuracy on real marine imagery

Effective noise suppression in noisy conditions

Enhanced robustness of multi-view learning algorithms

Abstract

To analyze marine animals behavior, seasonal distribution and abundance, digital imagery can be acquired by visual or Lidar camera. Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.), or dissimilarity matrices derived from different shape analysis methods (shape context, internal distance shape context, etc.). For both cases, multi-view learning is critical in integrating more than one set of feature or dissimilarity matrix for higher classification accuracy. This paper adopts correntropy loss as cost function in multi-view learning, which has favorable statistical properties for rejecting noise. For the case of features, the correntropy loss-based multi-view learning and its entrywise variation are developed based on the multi-view intact space learning algorithm. For the case of dissimilarity matrices, the robust Euclidean embedding algorithm is extended to its multi-view form with the correntropy loss function. Results from simulated data and real-world marine animal imagery show that the proposed algorithms can effectively enhance classification rate, as well as suppress noise under different noise conditions.

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