On the efficacy of handcrafted and deep features for seed image classification

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework

Decomposition of two-dimensional shapes for efficient retrieval

This paper presents a novel approach to address the problem of generic 2D shape recognition. We propose a morphological method to decompose a binary shape into entities in correspondence with their protrusions. Each entity is associated with a set of perceptual features that can be used in indexing into image databases. The matching process, based on the softassign algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and articulated structures. In order to increase efficiency, the retrieval process is applied after a coarse scale grouping of objects, without sacrificing effectiveness and allowing indexing into large shape databases. (C) 2008 Elsevier B.V. All rights reserved

Decomposition of two-dimensional shapes for efficient retrieval

This paper presents a novel approach to address the problem of generic 2D shape recognition. We propose a morphological method to decompose a binary shape into entities in correspondence with their protrusions. Each entity is associated with a set of perceptual features that can be used in indexing into image databases. The matching process, based on the softassign algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and articulated structures. In order to increase efficiency, the retrieval process is applied after a coarse scale grouping of objects, without sacrificing effectiveness and allowing indexing into large shape databases