5,527 research outputs found
Coral classification with hybrid feature representations
© 2016 IEEE. Coral reefs exhibit significant within-class variations, complex between-class boundaries and inconsistent image clarity. This makes coral classification a challenging task. In this paper, we report the application of generic CNN representations combined with hand-crafted features for coral reef classification to take advantage of the complementary strengths of these representation types. We extract CNN based features from patches centred at labelled pixels at multiple scales. We use texture and color based hand-crafted features extracted from the same patches to complement the CNN features. Our proposed method achieves a classification accuracy that is higher than the state-of-art methods on the MLC benchmark dataset for corals
Sparse Coral Classification Using Deep Convolutional Neural Networks
Autonomous repair of deep-sea coral reefs is a recent proposed idea to
support the oceans ecosystem in which is vital for commercial fishing, tourism
and other species. This idea can be operated through using many small
autonomous underwater vehicles (AUVs) and swarm intelligence techniques to
locate and replace chunks of coral which have been broken off, thus enabling
re-growth and maintaining the habitat. The aim of this project is developing
machine vision algorithms to enable an underwater robot to locate a coral reef
and a chunk of coral on the seabed and prompt the robot to pick it up. Although
there is no literature on this particular problem, related work on fish
counting may give some insight into the problem. The technical challenges are
principally due to the potential lack of clarity of the water and platform
stabilization as well as spurious artifacts (rocks, fish, and crabs). We
present an efficient sparse classification for coral species using supervised
deep learning method called Convolutional Neural Networks (CNNs). We compute
Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component
Analysis (ZCA) Whitening to extract shape and texture feature descriptors,
which are employed to be supplementary channels (feature-based maps) besides
basic spatial color channels (spatial-based maps) of coral input image, we also
experiment state-of-art preprocessing underwater algorithms for image
enhancement and color normalization and color conversion adjustment. Our
proposed coral classification method is developed under MATLAB platform, and
evaluated by two different coral datasets (University of California San Diego's
Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).Comment: Thesis Submitted for the Degree of MSc Erasmus Mundus in Vision and
Robotics (VIBOT 2014
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Automatic Hierarchical Classification of Kelps utilizing Deep Residual Feature
Across the globe, remote image data is rapidly being collected for the
assessment of benthic communities from shallow to extremely deep waters on
continental slopes to the abyssal seas. Exploiting this data is presently
limited by the time it takes for experts to identify organisms found in these
images. With this limitation in mind, a large effort has been made globally to
introduce automation and machine learning algorithms to accelerate both
classification and assessment of marine benthic biota. One major issue lies
with organisms that move with swell and currents, like kelps. This paper
presents an automatic hierarchical classification method (local binary
classification as opposed to the conventional flat classification) to classify
kelps in images collected by autonomous underwater vehicles. The proposed kelp
classification approach exploits learned feature representations extracted from
deep residual networks. We show that these generic features outperform the
traditional off-the-shelf CNN features and the conventional hand-crafted
features. Experiments also demonstrate that the hierarchical classification
method outperforms the traditional parallel multi-class classifications by a
significant margin (90.0% vs 57.6% and 77.2% vs 59.0%) on Benthoz15 and
Rottnest datasets respectively. Furthermore, we compare different hierarchical
classification approaches and experimentally show that the sibling hierarchical
training approach outperforms the inclusive hierarchical approach by a
significant margin. We also report an application of our proposed method to
study the change in kelp cover over time for annually repeated AUV surveys.Comment: MDPI Sensor
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