2,360 research outputs found
A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
Recently, many approaches have been introduced by several researchers to
identify plants. Now, applications of texture, shape, color and vein features
are common practices. However, there are many possibilities of methods can be
developed to improve the performance of such identification systems. Therefore,
several experiments had been conducted in this research. As a result, a new
novel approach by using combination of Gray-Level Co-occurrence Matrix,
lacunarity and Shen features and a Bayesian classifier gives a better result
compared to other plant identification systems. For comparison, this research
used two kinds of several datasets that were usually used for testing the
performance of each plant identification system. The results show that the
system gives an accuracy rate of 97.19% when using the Flavia dataset and
95.00% when using the Foliage dataset and outperforms other approaches.Comment: 10 page
Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
The present work proposes the development of a novel method to provide
descriptors for colored texture images. The method consists in two steps. In
the first, we apply a linear transform in the color space of the image aiming
at highlighting spatial structuring relations among the color of pixels. In a
second moment, we apply a multiscale approach to the calculus of fractal
dimension based on Fourier transform. From this multiscale operation, we
extract the descriptors used to discriminate the texture represented in digital
images. The accuracy of the method is verified in the classification of two
color texture datasets, by comparing the performance of the proposed technique
to other classical and state-of-the-art methods for color texture analysis. The
results showed an advantage of almost 3% of the proposed technique over the
second best approach.Comment: Chaos, Volume 21, Issue 4, 201
Texture analysis by multi-resolution fractal descriptors
This work proposes a texture descriptor based on fractal theory. The method
is based on the Bouligand-Minkowski descriptors. We decompose the original
image recursively into 4 equal parts. In each recursion step, we estimate the
average and the deviation of the Bouligand-Minkowski descriptors computed over
each part. Thus, we extract entropy features from both average and deviation.
The proposed descriptors are provided by the concatenation of such measures.
The method is tested in a classification experiment under well known datasets,
that is, Brodatz and Vistex. The results demonstrate that the proposed
technique achieves better results than classical and state-of-the-art texture
descriptors, such as Gabor-wavelets and co-occurrence matrix.Comment: 8 pages, 6 figure
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
In this work, we propose a novel technique for obtaining descriptors of
gray-level texture images. The descriptors are provided by applying a
multiscale transform to the fractal dimension of the image estimated through
the probability (Voss) method. The effectiveness of the descriptors is verified
in a classification task using benchmark over texture datasets. The results
obtained demonstrate the efficiency of the proposed method as a tool for the
description and discrimination of texture images.Comment: 7 pages, 6 figures. arXiv admin note: text overlap with
arXiv:1205.282
Plant image retrieval using color, shape and texture features
We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques
and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered
Sabanci-Okan system at ImageClef 2011: plant identication task
We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results
are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
In this paper, we employ Probabilistic Neural Network (PNN) with image and
data processing techniques to implement a general purpose automated leaf
recognition algorithm. 12 leaf features are extracted and orthogonalized into 5
principal variables which consist the input vector of the PNN. The PNN is
trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater
than 90%. Compared with other approaches, our algorithm is an accurate
artificial intelligence approach which is fast in execution and easy in
implementation.Comment: 6 pages, 3 figures, 2 table
FRACTAL DIMENSION AND LACUNARITY COMBINATION FOR PLANT LEAF CLASSIFICATION
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
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