Plant leaf recognition by integrating shape and texture features

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

Leaf Image Recognition Based on Bag of Features

Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance.

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Shape Matching Based on Multi-Scale Invariant Features

Here, we intend to propose local shape curve features which are invariant under planar Euclidean transformations and independent with respect to the original curve parameterization. The present work generalizes the family of Curvature Scale Space descriptors in order to increase the shape information quantity to tend to the completeness property. For this, a more pragmatic criterion is introduced in this paper which we call the almost completeness. We define it as a pre-completeness for a given resolution of features. Such descriptors are formed by the curvatures on the set of curve points obtained from the antecedents of different curvature levels. This level set is fixed with a given rule. The idea of the almost completeness is to make a compromise between the cardinal of the set of curvature’s levels and the optimal number of scales. The rule is submitted to an unsupervised statistical study and the scales are obtained with a spectral analysis. Experiments are conducted on several known datasets. Promising results in the sense of shape retrieval and shape recognition rates are demonstrated.

An almost complete curvature scale space representation: Euclidean case

Invariant multiscale triangle feature for shape recognition

Shape information plays an important role in the human visual system, and how to represent the shape accurately is a very challenging issue in shape recognition. In recent years, many shape description approaches have been proposed. However, most of the existing methods are still facing challenges on accuracy and computational efficiency. Fourier descriptor is regarded as a promising shape description method because it has a solid theoretical foundation and at the same time possesses the strength of appealing invariance properties and high computational efficiency. We introduce a novel multiscale Fourier descriptor based on triangular features which is used to identify shapes. The local and global characteristics of a shape are effectively captured by the proposed shape descriptor. Meanwhile, the proposed shape descriptor has good properties, such as the invariant of the geometric transformation and the starting point of an object. We tested this descriptor on four popular benchmarking datasets, including MPEG-7, Swedish leaf, ETH-80, and Flavia leaf. The results confirm that our method is better than comparable-complexity approaches based on Fourier descriptors and does not perform unfavorably with respect to more complex, state-of-the-art, shape descriptors. In particular, our method is far superior to the complex shape description methods in terms of retrieval efficiency and computational complexity.

Chengzhuan Yang

Papers

Multiscale Fourier descriptor based on triangular features for shape retrieval