Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition

Kilic, Kemal Ihsan; Abiyev, Rahib H.

doi:10.1016/j.sigpro.2011.04.018

Cited by 52 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This concept defines that an object will be "lacunar" if gap (hole) on an object tends to be large. Low lacunarity indicates that the texture is homogeneous, while high lacunarity indicates that the texture is heterogeneous [13,20]. High lacunarity value means that the pixels spread out over a wider range and surrounded by many and large gaps [20].…”

Section: Lacunarity Measurementmentioning

confidence: 99%

“…Box counting method [19], is the most common approach used in calculating fractal dimension of an object, with its ability to represent the complexity of the image and its easy implementtation [20]. Therefore, Bruno et al [6] perform a leaf identification based on the complexity of the internal and external shape of leaf to obtain the fractal dimension using box counting method.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, Kilic and Abiyev [20] mention that the fractal dimension and lacunarity have been examined separately, and there is no significant effort in combining the two features in a better synergy. Therefore, this study proposes the combination of fractal dimension features of leaf shape and lacunarity features of leaf texture to improve the classification accuracy compared to previous fractal dimension and lacunarity methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Muchtar

Suciati

Fatichah

2016

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

Plants play important roles for the existence of all beings in the world. High diversity of plant's species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant's leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy. Keywords: leaf classification, fractal dimension, lacunarity, box counting, gliding box AbstrakTumbuhan memegang peranan penting dalam kehidupan manusia. Tingginya keberagaman spesies tumbuhan membuat metode pengamatan manual dalam klasifikasi daun menjadi semakin sulit. Dimensi fraktal merupakan deskriptor bentuk dan tekstur yang mampu mendeskripsikan kompleksitas dari suatu objek dalam bentuk dimensi pecahan. Di sisi lain, lacunarity adalah deskriptor tekstur berbasis fraktal yang mampu mendeskripsikan heterogenitas dari citra tekstur. Namun lacunarity belum cukup dieksplorasi dalam banyak kasus dan belum ada usaha yang cukup signifikan dalam mengkombinasikan dimensi fraktal dan lacunarity dalam bidang klasifikasi tumbuhan secara otomatis. Penelitian ini berfokus pada ekstraksi dan kombinasi fitur dimensi fraktal dan lacunarity untuk meningkatkan akurasi klasifikasi. Metode box counting diterapkan untuk memperoleh dimensi fraktal dari bentuk pinggiran dan urat daun, sementara metode gliding box diterapkan untuk memperoleh fitur lacunariy dari tekstur daun. menggunakan 626 citra daun dari flavia, percobaan dilakukan dengan menganalisis performa dari kedua fitur dengan mempertimbangkan ukuran kotak r yang paling optimal. Klasifikasi dengan support vector machine menunjukkan bahwa hasil kombinasi kedua fitur mampu mencapai rata-rata akurasi hingga 93.92%.

show abstract

Section: Lacunarity Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Muchtar

Suciati

Fatichah

2016

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

show abstract

“…A good example of this type of texture is the 112 texture images of the Brodatz album. This album provides a very useful natural texture database, which has been widely used to evaluate texture discrimination methods [30][31][32][33]. Texture from this album can be digitized into different graylevel intervals resulting in different background intensities.…”

Section: Normalized Grayscale Texturesmentioning

confidence: 99%

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Safia

2013

ISRN Machine Vision

View full text Add to dashboard Cite

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

show abstract

“…The method performance was assessed on four well-known image databases (Brodatz [24], Vistex [25], UIUC [33] and UMD [16]) and the results were compared to other texture descriptors reported in the literature (Gabor [26], Fourier [27], Grey-Level Cooccurrence Matrix (GLCM) [28], Multifractal [16], Local Binary Patterns (LBP) [29], Soft-LBP [35], Fuzzy-LBP [36] and textons (VZ) [34]). …”

Section: Introductionmentioning

confidence: 99%

Texture descriptors by a fractal analysis of three-dimensional local coarseness

Florindo

Landini

Bruno

2015

Digital Signal Processing

View full text Add to dashboard Cite

This work proposes a new method of extracting texture descriptors from digital images based on local scaling properties of the greyscale function using constraints to define connected local sets. The texture is first mapped onto a three-dimensional cloud of points and the local coarseness under different scales is assigned to each point p. This measure is obtained from the size of the largest "connected" set of points within a cube centred at p. Here, the "connected set" is defined as the set of points such that for each point in the local domain there is at least one other point at a distance smaller than a threshold t. Finally, the Bouligand-Minkowski fractal descriptors of the local coarseness of each pixel are computed. The classificatory power of the descriptors on the Brodatz, Vistex, UIUC and UMD databases showed an improvement over the results obtained with other well-known texture descriptors reported in the literature. The performance achieved also suggests possible applications to real-world problems where the images are best analysed as textures.

show abstract

Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition

Cited by 52 publications

References 19 publications

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Texture descriptors by a fractal analysis of three-dimensional local coarseness

Contact Info

Product

Resources

About