942 research outputs found
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
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
Experiments of Distance Measurements in a Foliage Plant Retrieval System
One of important components in an image retrieval system is selecting a
distance measure to compute rank between two objects. In this paper, several
distance measures were researched to implement a foliage plant retrieval
system. Sixty kinds of foliage plants with various leaf color and shape were
used to test the performance of 7 different kinds of distance measures: city
block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2
statistics, Jensen Shannon divergence and Kullback Leibler divergence. The
results show that city block and Euclidean distance measures gave the best
performance among the others.Comment: 14 pages, International Journal of Signal Processing, Image
Processing and Pattern Recognition Vol. 5, No. 2, June, 201
Dean Diepeveen survey of computer-based vision systems for automatic identification of plant species
Feature extraction and automatic recognition of plant leaf using artificial neural network
Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant leaf morphology (such as shape, dent, margin, vein and so on), domain-related visual features of plant leaf are analyzed and extracted first. On such a basis, an approach for recognizing plant leaf using artificial neural network is brought forward. The prototype system has been implemented. Experiment results prove the effectiveness and superiority of this method
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