Plant species identification using leaf image retrieval

Caballero, Carlos; Aranda, M. Carmen

doi:10.1145/1816041.1816089

Cited by 54 publications

(33 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the fact that leaves are present on the plants for at least several months, which is not generally the case for other organs such as fruits or flowers. Therefore, most plant identification tools based on Content-Based Image Retrieval techniques [20,27,5,8,7,3,9,10] work on leaf image databases. Leaves can be characterized by their shape, color and texture.…”

Section: Introductionmentioning

confidence: 99%

“…To describe the shape of a leaf, one can develop a specific approach or adapt a generic shape retrieval method to the particular case of leaves. Specific approaches [13,8] are based on the botanical characterization of leaf shapes. They extract morphological characters such as: Aspect Ratio, Rectangularity, Convex Area Ratio, Convex Perimeter Ratio, Sphericity, Circularity, Eccentricity and Form Factor.…”

Section: Introductionmentioning

confidence: 99%

“…When global features are extracted, a global measure is used to compute the similarity of the shapes. The Curvature Scale Space approach [28] has been tested on leaves in [28,8]. Fourier-based descriptors have also been used [41,32,30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A shape-based approach for leaf classification using multiscaletriangular representation

Mouine

Yahiaoui

Verroust-Blondet

2013

Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

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-ofart methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A shape-based approach for leaf classification using multiscaletriangular representation

Mouine

Yahiaoui

Verroust-Blondet

2013

Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

show abstract

“…Moreover, leaves are present on the plants for several months in a year, whereas flowers and fruits may remain only several weeks. This is why most plant identification tools based on Content-Based Image Retrieval techniques work on leaf image databases [17,29,20,24,31,10,4,7,23,25,6,3,8,9,13]. A leaf can be characterized by its color, its texture, and its shape.…”

Section: Introductionmentioning

confidence: 99%

“…Morphological features are also retained and used in the identification process in the parameterized segmentation representation of leaves proposed by Cerruti et al [9]. Eccentricity is used in the two-stage approach of Wang et al [29] and of Caballero and Aranda [7] to reduce the search space. Other shape feature extraction techniques [19] have been adapted or introduced to solve the plant retrieval problem.…”

Section: Introductionmentioning

confidence: 99%

Advanced shape context for plant species identification using leaf image retrieval

Mouine

Yahiaoui

Verroust-Blondet

2012

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

This paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectivness of our approach.

show abstract

Decoding family‐level features for modern and fossil leaves from computer‐vision heat maps

Spagnuolo

Wilf

Serre

2022

American J of Botany

View full text Add to dashboard Cite

Premise Angiosperm leaves present a classic identification problem due to their morphological complexity. Computer‐vision algorithms can identify diagnostic regions in images, and heat map outputs illustrate those regions for identification, providing novel insights through visual feedback. We investigate the potential of analyzing leaf heat maps to reveal novel, human‐friendly botanical information with applications for extant‐ and fossil‐leaf identification. Methods We developed a manual scoring system for hotspot locations on published computer‐vision heat maps of cleared leaves that showed diagnostic regions for family identification. Heat maps of 3114 cleared leaves of 930 genera in 14 angiosperm families were analyzed. The top‐5 and top‐1 hotspot regions of highest diagnostic value were scored for 21 leaf locations. The resulting data were viewed using box plots and analyzed using cluster and principal component analyses. We manually identified similar features in fossil leaves to informally demonstrate potential fossil applications. Results The method successfully mapped machine strategy using standard botanical language, and distinctive patterns emerged for each family. Hotspots were concentrated on secondary veins (Salicaceae, Myrtaceae, Anacardiaceae), tooth apices (Betulaceae, Rosaceae), and on the little‐studied margins of untoothed leaves (Rubiaceae, Annonaceae, Ericaceae). Similar features drove the results from multivariate analyses. The results echo many traditional observations, while also showing that most diagnostic leaf features remain undescribed. Conclusions Machine‐derived heat maps that initially appear to be dominated by noise can be translated into human‐interpretable knowledge, highlighting paths forward for botanists and paleobotanists to discover new diagnostic botanical characters.

show abstract

Plant species identification using leaf image retrieval

Cited by 54 publications

References 8 publications

A shape-based approach for leaf classification using multiscaletriangular representation

A shape-based approach for leaf classification using multiscaletriangular representation

Advanced shape context for plant species identification using leaf image retrieval

Decoding family‐level features for modern and fossil leaves from computer‐vision heat maps

Contact Info

Product

Resources

About