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

K-Means Color Segmentation and Morphological Feature Extraction of Bamboo Fiber as an Environmentally Friendly Material for Soil Strengthening

Indonesia is one of the bamboo producing countries. One of the characteristics of good bamboo is that bamboo has a good fiber content as well. Characteristics of a good bamboo fiber is fiber that has a size and length of fiber that is large and long because it will affect the compressive strength and tensile strength of the bamboo. This study uses the K-Means color segmentation method and shape measurements based on the morphological characteristics of bamboo fibers, namely the area, circumference / perimeter and fiber roundness ratio. The results of testing with 18 images of bamboo type training show that Wulung bamboo fiber which is still of the same genus with bamboo Gombong has the largest size fiber of 40.4, the longest perimeter of 3.73 and has a roundness ratio of 0.83 bamboo Ori which has an area of 21, 6, perimeter of 3.23 and a roundness ratio of 0.79. Bambu Petung has an area of 20.6, perimeter of 2.53 and roundness ratio of 0.79. Java Bamboo has an area of 20.2, perimeter of 1.19 and a roundness ratio of 0.8. Whereas Bambu Apus only has an area of 19.2, a perimeter of 2.09 and a roundness ratio of 0.78. Testing using 8 testing images obtained an accuracy rate of  0,625

Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR), but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR). In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved

Identifikasi Gejala Penyakit Tanaman Jeruk Melalui Pengolahan Citra

Pengolahan citra adalah trend terkini mendukung suatu pengenalan pola objek citra secara digital, dengan penerapan metode dan konsep dalam menginterprestasikan informasi menjadi pendukung data secara visual. Gejala penyakit pada tanaman dapat terlihat adanya noda pada area objek, sehingga dalam memudahkan pengenalan fitur yang digunakan adalah dengan tekstur, karena tanda penyakit dapat mengenai sekitar atau seluruh area obyek. Usulan yang dibangun diharapkan dapat memberikan solusi untuk melakukan identifikasi gejala suatu penyakit melalui pengolahan citra, dengan melibatkan konsep dan metode. Tahapan yang diterapkan dalam pengelolaan adalah preprocessing, feature extraction, dan identification Metode preprocessing dilakukan dengan resize, clipping, penajaman tekstur dengan usharp mask filter dan konversi RGB ke gray. Feature extraction dengan metode Fast Fourier Transform (FFT)  dan Local Binary Pattern (LBP). FFT merupakan ekstraksi cepat pada transformasi fourier, sedangkan LBP merupakan ekstraksi ciri dengan diskripsi pola pada citra gray. Proses  identifikasi dengan metode Probabilistic Neural Network (PNN) dalam melakukan klasifikasi yang mendukung proses identifikasi terhadap penyakit tanaman, jumlah data yang digunakan 233, terbagi dalam 157 data latih dan 76 data uji. Hasil klasifikasi terhadap data latih menunjukan hasil maksimal untuk semua citra batang, daun, dan buah. Sedang untuk data uji hasilnya tertinggi identifikasi pada penerapan ekstraksi ciri dengan FFT dibandingkan dengan LBP ataupun gabungan kedua ekstraksi ciri tersebut

Identifying Medicinal Plant Leaves Using Textures and Optimal Colour Spaces Channel

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT). Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces

Segmentasi Canny Dan Otsu pada Citra Daun Jeruk Tidak Sehat

Pengolahan objek digital merupakan dasar dari identifikasi citra digital, melibatkan knowledge sebagai interprestasi informasi secara visual dengan menggunakan metode yang terkait untuk diimplementasikan. Daun jeruk yang tidak sehat disebabkan oleh gejala penyakit tanaman, dengan ditandai adanya noda (spot). Adanya kemiripan bentuk dan warna daun antara citra, maka penciri tekstur digunakan sebagai dasar dalam penelitian ini.Sistem yang dibangun diharapkan sebagai solusi untuk melakukan segmentasi terhadap daun tanaman jeruk melalui pengolahan citra, dengan melibatkan metode dan konsep. Tahapan proses yang dilakukan adalah file name, preprocessing, dan segmentation. Metode preprocessing dengan resize, clipping, normalisasi dan RGB ke gray. Segmentation dengan menggunakan Canny dan Otsu.Hasil pengamatan citra sejumlah 20 data untuk kategori penyakit Kudis 10 data , dan penyakit Kanker 10 data. Analisa hasil segmentasi Canny dapat mendeteksi jelas bercak pada noda gejala penyakit Kanker dan hanya sebagian dari penyakit Kudis. Segmentasi Otsu dapat mendeteksi jelas bercak pada noda gejala penyakit Kudis dan juga Kanker. Segmentasi dengan Canny, dipengaruhi juga oleh tulang dan lekukan daun yang tebal, sehingga dapat menimbulkan tepian yang muncul dan berdekatan pada noda penyakit Kudis. Segmentasi Otsu, dipengaruhi juga oleh lekukan dan ketebalan daun, sehingga dapat menimbulkan warna gelap yang muncul dan berdekatan pada noda penyakit

Image Retrieval in Mobiles using Signature based Approach

Abstract -Since camera based handheld devices are widely used in today's world, and we also tend to click pictures and store it. Hence there is a need for a system that could process the pictures clicked from a hand-held device and retrieve back similar images from a central image database along with the information tagged with it. Mobile phones have very limited display size and limited number of control keys, so most of these systems encounter serious difficulties for both presenting the query image and also showing the retrieval results. In this paper, we describe a way in which a captured image can be searched in the web using content based retrieval system