In this paper, a 1d convolutional neural network is designed for classification tasks of leaves with centroid contour distance curve (CCDC) as the single feature. With this classifier, simple feature as CCDC shows more discriminating power than people thought previously. The same architecture can also be applied for classifying 1 dimensional time series with little changes. Experiments on some benchmark datasets shows this architecture can provide classification accuracies that are higher than some existing methods. Code for the paper is available at this https URL Project.
On the side of shape features, they can be extracted based on botanical characteristics @cite_20 @cite_11 . These features may include: Aspect Ratio, Rectangularity, Convex Area, Ratio, Convex Perimeter Ratio, Sphericity, Circularity, Eccentricity, Form Factor, etc. @cite_1 discussed some other features applied on leave shapes and introduced two new multiscale triangle representations. There are also a lot of other work done with more in-depth design aiming for general shapes than just leaves. @cite_25 defines inner distance of shape contours to build shape descriptors. @cite_12 develops the visual descriptor called CENTRIST (CENsus TRansform hISTogram) for scene recognitions, it get good performance when applied to leave images. Authors of @cite_21 uses the transformation form shape contours to 1 dimensional time series and present the method of shapelet for shape recognition. @cite_24 describes a hierarchical representation for two dimensional objects that captures shape information at multiple levels of resolution for matching deformable shapes. Features coming from different method can be stacked together, these bagged features can usually help provide better performance as discussed in @cite_3 .
@cite_21 @cite_1 @cite_3 @cite_24 @cite_20 @cite_25 @cite_12 @cite_11
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Psychophysical studies show that we can recognize objects using fragments of outline contour alone. This paper proposes a new automatic visual recognition system based only on local contour features, capable of localizing objects in space and scale. The system first builds a class-specific codebook of local fragments of contour using a novel formulation of chamfer matching. These local fragments allow recognition that is robust to within-class variation, pose changes, and articulation. Boosting combines these fragments into a cascaded sliding-window classifier, and mean shift is used to select strong responses as a final set of detection. We show how learning can be performed iteratively on both training and test sets to bootstrap an improved classifier. We compare with other methods based on contour and local descriptors in our detailed evaluation over 17 challenging categories and obtain highly competitive results. The results confirm that contour is indeed a powerful cue for multiscale and multiclass visual object recognition. In this paper, we propose a novel framework for contour based object detection. Compared to previous work, our contribution is three-fold. 1) A novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. 2) Grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. 3) A novel local affine-transformation to utilize the holistic shape information for scoring and ranking the shape similarity hypotheses. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. Very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background. This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET'97. In this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods.