Mushroom Recognition Using PCA Algorithm

Transcription

1 , pp Mushroom Recognition Using PCA Algorithm Ashwin Subramaniam 1 and Byung-Joo Oh * Department of Electronics Engineering, Hannam University, Daejeon, Korea 1 ashwinksk@yahoo.com, * bjoh@hnu.kr Abstract This paper proposes a method of distinguishing edible mushrooms from non-edible mushrooms using Principal Component Analysis (PCA) algorithm. This system functions by projecting a mushroom image onto a feature space that spans the significant variations among known set of mushroom images. Mushrooms possess certain significant features such as its stalk size, cap shape etc, and PCA extracts these dominant features and these are the eigenvectors of the set of mushrooms. The projection operation characterizes individual mushroom images by a weighted sum of the eigenvector features and hence to recognize a particular mushroom, so it is necessary only to compare these weights to those individual ones. The performance of the proposed method showed around 85% ~96% success rate that increases with the number of training images, and hence proves to be a reliable algorithm for the recognition of mushrooms. Keywords: Principal component analysis, Mushroom recognition, Mushroom features 1. Introduction Assume that you are walking in the woods and you see a mushroom on the ground, you would like to taste it but you have no idea what type of mushroom it is or whether it is good or harmful. Some mushrooms are poisonous in nature and few are psychoactive. There are a lot of mushrooms that are edible, some creates illness, some are fatal to life. But these fatal mushrooms are easy to come across and are deceptive in nature. While trekking or hiking in mountains it is quite certain that one could encounter these fatal mushrooms and misinterpret them to be safe to consume which may subsequently result in devastating effects. A large number of mushroom species are favored for eating by mushroom hunters. Many field guides on mushrooms are available, but the ability to identify and prepare edible mushrooms is often passed down through generations [1]. There are a few dominant features of a mushroom that distinguishes one from another, which include, Cap(Pileus), Fibrils or Scales or Warts on the cap, Ring(Annulus), Gills(Lamellae) under the cap, Stem(stripe) or Stalk, Cup(volva), Mycelial threads and so on strip. The central motive of this paper is to produce a precautionary methodology that can differentiate between edible, inedible and poisonous mushrooms, so as to provide a helping hand during mushroom hunting or consuming random mushrooms for smart phone applications and to protect the consumer from the fatality causing fleshy fungi. Among various recognition methods, principal component analysis (PCA) specially has been used popularly for face recognition [2-4], but unfortunately it is not easy to find applications related to wild mushroom recognition. PCA is a method used to emphasize * Corresponding Author ISSN: IJSEIA Copyright c 2016 SERSC

2 variation and bring out strong patterns in a dataset. It is a common statistical technique for finding the patterns in high dimensional data. Eigen-vectors have advantages over the other techniques available, such as speed and efficiency. In this paper we try to recognize wild mushrooms by applying PCA algorithm. It is the first step for a feasible study that can be adopted in the smart mobile phone. The organization of this paper is as follows. In Section 2 we introduce principal component analysis algorithm briefly and the flow of mushroom recognition. Mushroom databases for training and test are presented in Section 3. Our experimental results and analysis are presented in Section 4. Finally we conclude in Section Principal Component Analysis 2.1. Principal Component Analysis Work Flow Principal component analysis (PCA) identifies and extracts the major features of a mushroom which is obtained by determining the mean image of the mushroom and comparing it with the mushrooms present in the database. Mushroom Database Figure 1. Work Flow Model Principal component analysis (PCA)[2] involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. Using PCA any particular image can be (i) Economically represented along the eigen pictures coordinate space, and (ii) Approximately reconstructed using a small collection of eigen pictures. 44 Copyright c 2016 SERSC

3 To do this, a mushroom image is projected to several other mushroom templates called eigen-vectors which can be considered as a set of features that characterize the variation between the images. Once a set of eigen-vectors is computed, a mushroom image can be approximately reconstructed using a weighted combination of the eigen-vectors. The projection weights form a feature vector for mushroom representation and recognition. When a new test image is given, the weights are computed by projecting the image onto the eigenvectors. The classification is then carried out by comparing the distances between the weight vectors of the test image and the images from the database (Euclidian distance). Figure 1 shows work flow model of the mushroom recognition PCA Algorithm The algorithm used for PCA is as follows [2-6]. (i) Acquire an initial set of M mushroom images (the training set) and calculate the eigenvectors from the training set, keeping only M' eigen-vectors that correspond to the highest eigenvalue. (ii) Calculate the corresponding distribution in the M'-dimensional weight space for each known individual, and calculate a set of weights based on the input image. (iii) Classify the weight pattern as either a known person or as unknown, according to its distance to the closest weight vector of a known person. Let the training set of images be,,...,. The average image of the mushroom of the set is defined by, Each image differs from the average by vector i i (2) The co- variance matrix is formed by (1) Where the matrix A = [ This set of large vectors is then subject to principal component analysis, which seeks a set of M orthonormal vectors u 1, u 2,,u M. To obtain a weight vector of contributions of individual eigen-vectors to a mushroom image, the mushroom image is transformed into its eigen-vector components projected onto the mushroom image space by a simple operation k = u k T ( (4) for k=1,.., M', where M' M is the number of eigen-vectors used for the recognition. The weights form vector = [ ] that describes the contribution of each eigen-vectors in representing the mushroom image, treating the eigen-vectors as a basis set for (3) Copyright c 2016 SERSC 45

4 mushroom images. The simplest method for determining which mushroom provides the best description of an unknown input image of a mushroom is to find the image k that minimizes the Euclidean distance k. k = ( k ) 2 (5) where is a weight vector describing the k th mushroom from the training set. A mushroom is classified as belonging to k th type when the k is below some chosen threshold. The algorithm functions by projecting mushroom images onto a feature space that spans the significant variations among known mushroom images. The projection operation characterizes an individual mushroom by a weighted sum of eigen-vectors features, so to recognize a particular mushroom, it is necessary only to compare these weights to those of known individuals. The input image is matched to the subject from the training set whose feature vector is the closest within acceptable thresholds 3. Mushroom Databases There are two databases of mushrooms, one is the Train Database as in Figure 2 and the other is the Test Database. The test database has 30 mushrooms and each mushroom has 10 different images in the train database. Different mushrooms such as Ganoderma Luciduum, Amanita Muscaria, Enokii mushroom, Morel mushroom etc., were used in this study. Mushroom images in the train database were composed of different kind of positions and background conditions. This set of 300 different mushroom images, is a self-created database with the help of available images on the internet [7-9]. Considerable variations can be observed in the images as shown in Figure Test and Results Analysis The proposed mushroom recognition method was implemented in Matlab. The average image generated plays an essential role in comparison with the image to be identified via PCA algorithm. Since certain mushrooms like the bleeding tooth mushroom does not follow characteristics similar to the average image, the overall deviation from the mean image amplifies and makes the detection process tedious and erroneous. The images were converted from RGB to grayscale before performing the process of PCA, so that even if the resolution of the image is poor, the detection can still be successful. The 30 images of the test database were tested and 25 mushrooms were identified correctly and 5 were detected incorrectly Reasons for Successful Detection Correct detection as in Figure 4 could be descried for most of the mushrooms and the reason for this successful identification is due to consistent background conditions, lighting and sufficient amount of training image. Since more number of mushrooms is present in a single image it makes the process of comparison all the more efficient. 46 Copyright c 2016 SERSC

5 Figure 2. Mushroom Database (10 Images for each of the 30 Subjects) Copyright c 2016 SERSC 47

6 Figure 3. The Set of 10 Images for One Subject Moreover, mushroom such as the King Oyster Mushroom follows mushroom morphological trends and hence an average image would consist of an equal contribution of uniform images without irregularities in shape Reasons for Errors The reasons for incorrect detection of the mushroom as in Figure 5 may be due to i Inconsistent background conditions of a mushroom in the training database, which alters the significant features of that particular mushroom, while the process of comparison of its mean image. ii Number of train images and their quality. As the number of training images increase, the PCA detection success ratio experiences a proliferation. iii Irregularity in mushrooms as a whole. The distinctive features of a mushroom in general, as discussed earlier are the shape of its cup, stalk, ring (annulus) etc. and this is described by mushroom morphology or the study of mushrooms. Certain mushrooms do not follow this common pattern. For example, the bleeding tooth mushroom neither has a cup nor a stalk, and by nature appears in different odd shapes. PCA finds it difficult to extract specific features from such mushrooms for comparison. Figure 4. Mushrooms that Succeeded in the Recognition Process Figure 5. Mushrooms that were Failed to be Recognized Correctly 5. Analysis of Accuracy and Execution Time The test result shows an appreciably good execution time of approximately 42 seconds when around 10 images are provided for training purpose of one mushroom. The total memory size occupied by the database and the program as a whole sums up to 22.4MB, which most likely adapts to the smart phone standards. The accuracy of the system shows 48 Copyright c 2016 SERSC

7 Execution Time (seconds) Accuracy (%) International Journal of Software Engineering and Its Applications around 85% for a 10 image training set, but was observed to ameliorate substantially with increment in the number of training images. This is depicted in Table 1, and is represented graphically in Figure 6. Table 1. Variations in Accuracy and Execution Time with Respect to No. of Train Images No. of Accuracy Execution Images Time (sec) 10 85% % % % % 77 Variation of Execution Time Execution 85 Time Figure 6. Comparison of Execution Time and Accuracy with Respect to No. of No. of Images Train Images No. of Images 6. Conclusion Variations in Accuracy Accuracy This paper has discussed the extension of PCA algorithm to the recognition of mushrooms by extracting the significant characteristics of a mushroom using its eigenvectors. Predominantly, every mushroom has its own features that are consistent with the mushroom morphology theory, while few rare mushrooms show aberrations. The recognition was performed with differently oriented mushroom images with various positions and background conditions. It was observed that PCA showed around 85% ~96% success rate that increases with the number of training images, and hence proves to be a reliable algorithm for the detection of mushrooms. This paper concentrated PCA based mushroom recognition, subsequently in the near future we plan to combine PCA with neural networks. Having improvised with the detection we plan to have an interactive Graphical User Interface (GUI) based client server system to make this overall system user friendly for smart phone users. Copyright c 2016 SERSC 49

8 Acknowledgments This material is based on work supported by the 2012 Hannam University grant. References [1] [2] M. A. Turk and A. P. Petland, Eigen-faces for Recognition, Journal of Cognitive Neuroscience, vol. 3, (1991), pp [3] B. K. Gunturk, A. U. Batur and Y. Altunbasak, Eigenface-domain super-resolution for face recognition, IEEE Transactions of. Image Processing, vol. 12, no. 5, (2003), pp [4] L.-H. Chan, S.-H. Salleh and C.-M. Ting, Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis, Journal of Computer Science, vol. 6, no. 7, (2010), pp [5] B.-J. Oh, Face Recognition by Combining Principal Component Analysis and Multi-layer Neural Networks Classifiers, Journal of Korean Institute of Information Technology, vol. 3, no. 2, (2005), pp [6] A. Gunjan Dashore, B. Dr. V.Cyrril Raj, An efficient method for face recognition using principal component analysis (PCA), International Journal of Advanced Technology & Engineering Research, vol. 2, iss. 2, (2012), pp [7] [8] [9] Authors Ashwin Subramaniam 2014 SRM University, Kattankulathur, Chennai, Tamil Nadu, India Dept. of Electronics Engineering, Hannam University Daejeon, South Korea. Byung-Joo Oh B.S. Electronics Engineering, Busan National University, M.S. Electrical & Computer Engineering, University of New Mexico Ph.D, Electrical & Computer Engineering, University of New Mexico. 1988~ Senior Researcher, ETRI. 1992~Present, Professor in Electronics Engineering, Hannam University. Interest Area: Adaptive control, Neural network, Face detection and recognition, People counting. 50 Copyright c 2016 SERSC