Fuzzy Clustering for Image Segmentation Using Generic Shape Information

The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects

Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201