Identification of Freshwater Fish Types Using Linear Discriminant Analysis (LDA) Algorithm
Rini Nuraini*
Fakultas Teknologi Komunikasi dan Informatika, Program Studi Informatika, Universitas Nasional, Jakarta Selatan, Indonesia Email: [email protected]
Submitted: 21/11/2022; Accepted: 30/11/2022; Published: 30/11/2022
Abstract−Fish as aquatic animals have several physiological mechanisms that land animals do not have. Differences in habitat cause fish to adapt to environmental conditions, for example as animals that live in water, both in fresh and marine waters. The number of species or types of freshwater fish means knowledge of the types of freshwater fish. Identification of freshwater fish images is useful for the community, because the types of freshwater fish have different nutritional content, prices and processing for each type. Likewise for cultivators, identification of freshwater fish species can be useful for providing fish handling and management because each fish has a different cultivation method. The purpose of this study was to identify freshwater fish species using the Linear Discriminant Analysis (LDA) algorithm based on color feature extraction using HSV. The LDA algorithm has the ability to reduce dimensions by dividing data into several groups by maximizing the distance between groups that are different or more. To make the identification process easier, color feature extraction with HSV can be used to extract a variety of information from the color in the image. Based on the results of the accuracy test, it produces a value of 84.5%, which is included in the good category.
Keywords: Freshwater Fish; Image Identification; Linear Discriminant Analysis; Color Feature Extraction; HSV.
1. INTRODUCTION
Indonesia is known as a country that has abundant biodiversity. Biodiversity includes the diversity of ecosystems, species, and varieties. In the field of fisheries, Indonesia has biodiversity that lives in the sea and freshwater waters. According to a UN Food Agency report, the world's population would consume 19.6 kilogram of fish per person annually by 2021 [1].
Even though marine fish currently provide more fish for human consumption, freshwater fisheries will produce more fish in 2018 than seawater fisheries do. There are approximately 300 species of fish living in freshwater waters in Indonesia that live in lakes, rivers in the mountains and lowlands, and peat swamps [2]. Fish, as aquatic animals, have several physiological mechanisms that land animals do not have. Differences in habitat cause fish to adapt to environmental conditions, for example, as animals that live in water, both in fresh and marine waters [3]. Freshwater fish are fish that spend part or all of their lives in fresh water. The cultivation of freshwater fish is mostly done in Indonesia. The number of species or types of freshwater fish indicates knowledge of the types of freshwater fish. So, we need a system that can identify freshwater fish images. Identification of freshwater fish images is useful for the community because the types of freshwater fish have different nutritional content, prices, and processing methods. Likewise for freshwater fish cultivators, identification of freshwater fish species can be useful for providing fish handling and management because each fish has a different cultivation method. The types of freshwater fish can actually be identified based on their images because each type has characteristics that can be seen from its color. So, to find out the types of freshwater fish, digital image processing can be applied.
Digital image processing is defined as a field that examines image formation, manages an image, and analyzes images to gather information so that it can be beneficial to human [4]. Image identification is one of the uses for image processing. Image identification is a recognition procedure that involves categorizing items based on specific traits [5].
Previous research regarding the use of image processing on fish images has been carried out by several researchers. The first study regarding the identification of fish images containing formalin used the Multilayer Perceptron (MLP) algorithm with texture feature extraction Gray Level Co-Occurrence Matrix (GLCM), which obtained an accuracy value of 62%
[6]. The MLP method is considered capable of solving the problem of image identification because of its ability to search in a directed manner by considering the weight of obtaining an output in order to build a related system. However, the MLP method has a weakness in dealing with errors because every error that occurs can affect the weighting process [7].
Subsequent research, regarding the identification of images of local reef fish species by applying the backpropagation neural network method and the extraction of color and texture features, yielded an accuracy value of 88.73% [8]. The backpropagation neural network algorithm, which mimics the functioning of human nerves, has the capacity to identify patterns and convert a single input into an output that is taught depending on training. The training outcomes are not consistent because backpropagation neural networks cannot offer information about the weights that affect the input pattern [9]. Subsequent research, regarding the identification of the freshness level of tuna as seen from its image, uses the K-Nearest Neighbor (K-NN) approach for classification and the Gray Level Co-Occurrence Matrix (GLCM) method, which produces the highest accuracy rate of up to 60% [10]. The K-NN method classifies based on pattern learning from data that has already been classified, so K-NN is very dependent on the value of the features obtained; if the resulting features are redundant or irrelevant, it will affect the level of accuracy [11].
The difference between the research conducted and previous research is that this research focuses on identifying freshwater fish species by implementing the Linear Discriminant Analysis (LDA) algorithm. Hueld A. Fisher was the one who created this technique. LDA is a method for identifying patterns based on discovering combinations of characteristics [12]. LDA is a technique for machine learning, data processing, and image processing that makes use of statistical theory
[13]. With LDA, you can divide data into several groups. By eliminating redundant features and converting components from a higher dimensional space to a lower dimensional one, the LDA algorithm's primary objective is to reduce dimensionality [14]. By increasing the dissipation value across classes and reducing the spread between classes, the LDA method can isolate information between classes such that it is more independent. By increasing the value of each class, the LDA method has the benefit of being able to split data into different groups [15]. When employed to measure independent variables when each observation has a tendency to be continuous, the LDA method can perform at its best.
According to a number of earlier experiments, the algorithm appears to be capable of correctly classifying and identifying photos. The LDA method has demonstrated accurate image identification and classification in earlier experiments. An accuracy of 98.44% was attained in the study employing the LDA approach to introduce plant leaves [16]. Subsequent research to identify signature images uses the LDA method, which produces an average accuracy of 81% [17]. Other research involves research that conducts facial recognition by implementing the LDA model with an accuracy of 97.5%
[18].
In this work, freshwater fish species were identified using color feature extraction. In this work, hue and saturation measurements were employed to extract color features. To help with identification, color feature extraction with HSV is used to extract numerous details from an image's colors. Perceptual color spaces include HSV (Hue, Saturation, and Value). Hue, saturation, and value are the three-color channels that make up HSV's cylindrical coordinate system.
Additionally, by searching for patterns that can be divided into groups based on the boundary lines deduced from the linear equation, LDA will be able to enter spaces with smaller dimensions using the best projections.
2. RESEARCH METHODOLOGY
2.1 Research Stages
If the research is to be guided and carried out in accordance with the research objectives, it must be carried out via the stages of research in a logical and planned manner [19] To do this research properly, there are numerous stages that must be completed. Figure 1 depicts these steps.
Figure 1. Research Stages 2.1.1 Dataset Collection
The initial stage is to collect freshwater fish image data as a dataset, which will later be used for training and testing. This stage is very important because the availability of datasets is a determining factor for image processing performance. [20], [21]. The types of freshwater fish used are as many as 10 classes which are freshwater fish that are often consumed and cultivated in Indonesia based on the source of the suara.com website [22]. These types of freshwater fish include: Tilapia Fish, Patin Fish, Catfish, Goldfish, Parrot Fish, Pomfret, Wader Fish, Fish Cork, Eel Fish and Nilem Fish. In this study, the dataset used is 500 images. The distribution of the dataset uses 60% as training data and 40% as testing data. As a result, there are 30 images as training data and 20 images for each class.
2.2.2 L*a*b Image Transformation
Transformation using the L*a*b color space aims to digitally identify color content [23]. The color wheel produces 12 different sorts of colors, including red, yellow, green, cyan, blue, and magenta. The stages involve switching the image's
Accuracy Testing Image Classification with LDA
Feature Extraction with HSV Image Segmentation with
Thresholding L*a*b Image Transformation
Dataset Collection
color space from RGB to XYZ. Additionally, L*, a*, and b* values are calculated using the results of the RGB color values. The segmentation procedure is made easier with the help of this stage.
2.2.3 Image Segmentation with Thresholding
The process of segmenting digital photographs involves dividing them into smaller groupings known as segments. The requirements of image processing are typically the basis for the splitting or grouping procedure. The foreground and background of an image can be distinguished, or pixel sections might be grouped according to their common color or shape [24]. With this method, a limit value known as the threshold value is used. A grayscale image is threshold into a binary or black-and-white image so that the parts of the image that include objects and the backdrop can be distinguished easily [25]. The results of the threshold segmentation are binary images that have values 1 and 0. The equation used to convert image pixel values to binary in the segmentation process uses equation (1).
𝑔(𝑥, 𝑦) = {1, 𝑖𝑓 𝑓(𝑥, 𝑦) ≥ 𝑇
0, 𝑖𝑓 𝑓(𝑥, 𝑦) < 𝑇 (1)
were, 𝑓(𝑥, 𝑦) is a grayscale image 𝑔(𝑥, 𝑦), is a binary image, while 𝑇 denotes a threshold value.
2.2.3 Feature Extraction with HSV
HSV color features, which are based on hue and saturation values, are utilized to enhance the information in the feature extraction process. To help the identification process, color feature extraction with HSV is utilized to extract numerous details from the image's colors [26]. The Hue Saturation Value is used to extract color features (HSV). Perceptual color spaces include HSV (Hue, Saturation, Value). Hue, saturation, and value are the three-color channels that make up HSV's cylindrical coordinates. Based on the average hue and saturation values produced in the image, feature extraction is computed in this study. The employed mean feature is an equation (2).
= == M
i N
j
Iij
MN 1 1
1 (2)
2.2.5 Image Classification with LDA
Following the extraction of HSV features, color feature data will be gathered for the LDA algorithm's identification step.
The information index area is still present in LDA, but more classes are now frameable. Classes are isolated in order to increase the distance between them while reducing the distance required to provide information for a single class. The number of classes and postures used determine how many features the LDA algorithm produces. has been trained by LDA, and since LDA has no effect on the quantity of features created, processing it will take longer. There are various steps involved in putting the LDA approach into practice. The first step is to transform the test images and training image data into vectors (x1, x2, ..., xn). Next, make a class based on how many variables are present in both the test and training images. Then, using all of the currently available photos, the class average and overall class average (m) are determined.
After that, calculate the distribution matrix between classes (Sb) with equation (3).
)T mo )(mi mo mi k i
i( b n
S − −
= = 1
(3)
Then do the calculation of the distribution matrix in class (Sw) through equation (4).
T o j i o j i k
i n
j i
w n x m x m
S
i
) )(
( ( ) ( )
1 1
−
−
=
= =
(4)
Then proceed with projecting the distribution matrix within the class (Sw), where (Sw) is the distance matrix within the same class, using equation (5).
)) (
) ((
)
( 1
2 W maxtrace W S W W SW
J = T W − T b (5)
Then find the Eigenvalue (λ) and eigenvector value (υ) with equation (6).
w
b S
S = (6)
Then the eigenvalues (λ) are processed sequentially according to the order of values in the eigenvalues from the largest to the smallest. These results will be projected through the k-1 (υ) eigenvector (k denotes the number of classes).
The final process is to describe the entire original image into a fisher base vector through calculations by multiplying the point from the original image (V) to each fisher base vector xi using equation (7).
i t
x=V x
(7)
2.2.6 Accuracy Testing
Testing will be done at this stage in order to determine the performance of the model that is being created [27]. At this stage the effectiveness of the developed algorithm or model will be tested. Tests carried out are to measure the level of accuracy. To test accuracy use equation (8).
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠 × 100% (8)
3. RESULT AND DISCUSSION
This study identified images of freshwater fish using 10 classes, including: Mujair Fish, Catfish, Catfish, Goldfish, Tilapia, Pomfret Fish, Wader Fish, Snakehead Fish, Eel Fish and Nilem Fish. The process of collecting datasets is by taking images of freshwater fish using a camera one by one. The dataset used is 500 images. The training data used are 300 images or 30 images for each class. As for the test data as many as 200 images or for each class as many as 20 images.
The freshwater fish species identification model is implemented in MATLAB software, where the Linear Discriminant Analysis (LDA) algorithm will be applied with color feature extraction with HSV. The method of converting red, green, and blue images into L*a*b images will serve as the foundation for the first stage of image identification. This is done to allow for the digital identification of the color content. At this stage, the resulting transformation of the RGB image to L*a*b using the MATLAB software is shown in Figure 2.
(a) (b)
Figure 2. (a) RGB image and (b) Image that has been transformed into L*a*b
Image segmentation using thresholding approaches comes next. To make the feature extraction procedure easier, this method transforms image data into binary data. The result of this operation is a binary image, where the backdrop is 0 and the desired object is 1. The picture created via segmentation can be utilized as a mask to carry out the subsequent step. Figure 3 displays segmented image samples of various freshwater fish species.
(a) (b)
Figure 3. (a) L*a*b image and (b) Binary Image
The required object and its background can be distinguished converted into a binary image, as shown in Figure 3 (b). Additionally, the thresholding technique will determine the best threshold value so that the object and background can be distinguished from one another. It will be simpler to extract image objects once the image has been converted to binary. Utilizing HSV, features are extracted from the binary image. It aims to quickly categorize visual elements based on hue and saturation. In order to be easily grouped, the hue and saturation values are computed as average values. The hue and saturation values are shown in a table to give more information about the hue and saturation values in the image to be identified. Figure 4 displays the outcomes of the MATLAB application's feature extraction and calculation of the average hue and saturation values.
(a) (b)
Figure 4. (a) HSV Feature Extraction Results and (b) Displaying Feature Extraction Values
In Figure 4 (a) is the result of the average hue and saturation values obtained in the image. The LDA algorithm's identification procedure uses the feature extraction findings to look for class groups as a guide. LDA carries out reduction by removing unnecessary features and moving features from a higher dimensional space to a lower dimensional space.
The dataset is projected into a dimensional space with discrete feature classes in order to avoid overfitting. Classes can have a wide range of characteristics, and classification outcomes can result in a variety of overlapping variables.
Therefore, the feature must be improved to prevent overlapping. LDA tries to reduce dimensionality by deleting unused features and shifting parts from a higher dimensional space to a lower dimensional space. Figure 5 shows how LDA can isolate information between classes to make them more independent by reducing the spread between classes and increasing the dissipation value between classes.
Figure 5. LDA Separating Between Classes
The information index area is still present in LDA, but more classes are now frameable. Classes are isolated in order to increase the distance between them while reducing the distance required to provide information for a single class.
The number of classes and postures used determine how many features the LDA algorithm produces. Data that has been trained using LDA will take less time to process because LDA has no effect on the quantity of features created. Then, LDA is applied to the MATLAB program to be tested. The results of LDA implementation in MATLAB can be seen in Figure 6.
Figure 6. Developed Freshwater Fish Species Identification Application Interface
The next stage is to evaluate the developed model. Model evaluation is carried out to determine the performance of the model, where the model's performance will be measured using an accuracy test. The test data used are 200 images, where for each class of freshwater fish species tested with 20 images. Testing is carried out by matching the identification results by the system with existing facts. Accuracy calculations are obtained using equation (8), where the correct classification results are divided by the total number of tests then multiplied by 100. The test results from the results of freshwater fish identification can be seen in Figure 7.
Figure 7. Graph of Accuracy Test Results
Based on Figure 7 it can be seen that the accuracy test scores for each class that received the highest scores were for the Tilapia Fish, Catfish, Pomfret and Fish Cork classes with an accuracy value of 90%. Whereas for the class that gets the lowest score is Wader Fish with an accuracy value of 75%. The overall test accuracy average is 84.5%. The results obtained are then included in the accuracy criteria group based on the following criteria: Good, the value ranges
90% 85% 90%
80% 85% 90%
75%
90%
80% 80%
Tilapia Fish Patin Fish Catfish Goldfish Parrot Fish Pomfret Wader Fish Fish Cork Eel Fish Nilem Fish
from 76% to 100%; Simply, values range from 56% to 75%; Less Good, the value ranges from 40% to 55%, and Less Good, if the result is below 40% [28]. As a result, the LDA model's accuracy in identifying the species of freshwater fish falls into the "good" category. By increasing the distance between two or more groups, we were able to reduce dimensions and acquire these results by grouping the data into various categories. The separability of various zones in the dataset is then represented by a new axis that is created by the LDA algorithm. The average HSV value is used by the LDA algorithm to extract color feature information.
However, the error rate of the accuracy obtained reaches 15.5%. There are several factors that cause this error to occur, including: 1) The LDA algorithm only performs data reduction in creating data groups, so other factors are ignored;
2) Most types of freshwater fish have almost the same resemblance, thus requiring additional feature extraction, not only with color features; 3) The developed model requires a single object, if the data used has diverse backgrounds with various model viewpoints, it is difficult to identify; 4) The dataset used is relatively small so that the model is not maximized in learning.
4. CONCLUSION
This research implements the Linear Discriminant Analysis (LDA) method in classifying the maturity level of pineapple fruit based on color characteristics using the MATLAB application. The results of the accuracy test, which are 84.5%, fall into the "good" category. Utilizing color feature extraction with the HSV algorithm makes it possible to extract a variety of data from the image's colors, making the identification process easier. Then LDA is able to reduce dimensions by dividing the data into several groups and maximizing the distance between different or more groups. Create a new axis in LDA to represent the division of various zones in the dataset. The number of classes and postures created by LDA determine how many features are produced. For further research, it requires several improvements, including making improvements to the LDA algorithm, especially by combining it with an algorithm that can search data based on proximity in order to improve pattern recognition properly. In addition, the use of deep learning can be an alternative to optimizing feature extraction and pattern recognition. For large datasets, it is necessary to do experiments with larger datasets so that learning outcomes can be optimal.
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