Under Water Fish Species Recognition

International Journal of Pure and Applied Mathematics Volume 118 No. 7 2018, 357-361 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue

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Under Water Fish Species Recognition Sarika.S.Nair1,Sreenarayanan.N.M2,Fepslin Athish Mon3 and K.Suthendran4 Department of Computer science Engineering1,2,3 Department of Information Technology4 Royal college of Engineering and Technology,Thrissur,Kerala Kalasalingam Academy of Research and Education,India [email protected], [email protected], [email protected] [email protected]

Abstract— Live fish recognition plays an important role in fisheries survey. Challenges to under water fish recognition includes poor image quality, uncontrolled objects that occur in an image. Machine learning based approach used here to identify the fish species. SURF (Speedup robust features) used to extract features from fish image. Support vector machine (Supervised learning method) used for the underwater fish species classification. This paper aims to provide an efficient fish species identification method, and tries to avoid the problem of segmentation, feature extraction and classification while identifying underwater fish identification. Keywords-Feature extraction, Fish classification, Live fish recognition, I. INTRODUCTION In fisheries survey applications underwater fish recognition plays an important role. Large amount of data is required for classification process. There are several challenges for collecting representative samples for the same. It includes poor image quality, uncontrolled objects in the environment. Existing feature extraction techniques involves frequent human supervision. It requires more time and human effort to detect the same. Different species of fish image taken from photos and videos are manually analyzed by marine biologists to find useful information. This requires more time and effort. The system of recognized fish image may benefit to various fields. Some fish species may use to medical purpose also. So it is necessary to identify the species of fish from the underwater fish image. Previous system groups the fish image in to clusters based on size, shape .This also help to categorize the fish species in to poison or non-poison fish. Fish

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recognition and classification still active area in the agriculture domain also. Feature extraction takes place an important role in identifying the important features of fish image. Image features classified into two types: natural and artificial ones. Visual appearance defines the natural features are artificial features are obtained from some manipulations of an image such as image amplitude histogram and filters. The interesting point thus obtained from the feature extraction is feed to the classifiers to classify in to appropriate clusters. In this paper SURF algorithm used for the feature extraction and support vector machine used for the classification process. Section II discusses the literature survey. Then in section III describes the proposed method. In the section IV we discuss results and screenshot of this method and finally section V include the conclusion. II. LITERATURE SURVEY M.-C.Chuanget.al.[1]proposed two different fish feature extraction methods,supervised and unsupervised approaches,for classification which are then applied to a hierarchical partial classification framework.Specified anatomical parts of fish are automatically located to generate the supervisedfeature descriptor, which means a part aware feature extraction method,isused. These having some weakness including lack of generality and risk of suboptimal performance. For unsupervised feature extraction, a scale-invariant object part learning algorithm is proposed to discover common shape of fish body parts and then extract appearance,location and size Information of each part. Both are fed to partial hierarchical classifier. Experiments show that better recognition performance on live fish images collected by cameras are achieved by the unsupervised approach.A 10-cross validation is performed on the data set for classifier training.The HOG (histogram oriented techniques) isused to represent the appearance of body parts.The unsupervised method uses 4,6,8,and 10 cross

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validated object part learning. To model both the appearance and geometry this paper introduces the concept of an objective function .which takes three factors into account fitness, separation and discrimination. D.-J.Leeetal.[2]provide a contour matching method for fish recognition. Such a system includes automatic fish image acquisition, fish categorization, contour extraction and data storage. Shape is an import ant characteristic and shape analysis and shape matching are studied for the recognition of fish. Several shape descriptors, such as Fourier descriptors,line segments and polygon approximation, are tested. Previous works are focused on finding critical landmark points on fish shape using curvature function analysis landmark points on fish body. However, the main difficulty of this approach is that landmark points sometimes cannot locate accurately. Image acquisition including design, integration, contour extraction and fish categorization are discussed here. Fish categorization results based on shape analysis and shape matching are also included in this paper. M.S.Neryetal.[3]presents a fish classification methodology based on a robust feature selection technique. Determining the appropriate feature set for fish classification tasks determines the detection accuracy. Four feature extraction techniques are mentioned on this paper are size shape texture and color. Size measurement takes 4 features shape measurement takes 19 features, color and texture measurement contain 8 and16 features respectively .for the size measurement which includes planar measurement s on the fish mask area, center of the mask, its length and width its perimeter. These features are not invariant under translation, rotation and scale. For shape measurements it takes four parameters such as Rectangle fit factor, Circularity, Aspect ratio and moment. Both YUV and HSI color models were used for the color signature. Wong PohLee et.al[4] used GCFD(Generalized Color Fourier Descriptor) as color object descriptor. The use of GCFD is proposed, because of their robustness for describing color features of objects that is detected. By analyzing the frequencies, the non-related information is identified by performing some spectrum changes on the frequencies. The required objects in this paper are fish. The frequencies which corresponding to the fish is maintained while other frequencies are removed from the frequency domain. After removing the non-related frequencies, for further image processing the frequency domain is inversed in order to obtain the required image. GCFD issued as a descriptor to extract the features of the fish as it is invariant to rotation and translation. C. Lianet.al [5] investigates a ways to learn efficiently from uncertain data using belief functions. In order to extract more knowledge from imperfect information and to improve classification accuracy, here proposed a supervised learning method composed of a feature selection procedure and a two-step classification strategy. Using the training

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information,the feature selection procedure automatically determines the most informative feature subset by minimizing the objective function. III. PROPOSED SYSTEM The proposed method contains four modules. First module is preprocessing, in which the noise from the underwater fish image is removed and the second module is segmentation, which avoid the problem of segmentation the controlled object in background can be segmented using this. In the feature extraction phase important features of the fish image is extracted and it given to the classification phase.

Fig (3.1) System

architecture

Pre-Processing Underwater image noise removal is a wide area of research. The pre-processing is required for underwater images due to poor captured image quality. Speckle noise, Gaussian noise and Salt & pepper noise are the commonly occurring noises in underwater images. Edge preserving algorithms are required to preserve the edge point of object in an image. Here uses a Gaussian filter which removes the Gaussian noise from an image. Mostly occurring noise in an image is Gaussian noise. Filter improves the image quality, suppressed the noise, preserves the edges in an image, enhance and smoothen the image [6]. Gaussian filter is a windowed filter which places over the image. Sigma defines the amount of blurring. The radius slider is used to control how large the template is. Large values for sigma will only give large blurring for larger template sizes. Noise can be added using the sliders. Multiply


elements by corresponding window weights, then sum the products, this sum is new filtered value. Segmentation Uncontrolled object in the environment problem can be somewhat solved using segmentation method. K-Means clustering algorithm used for he segmentation process. Compared to other methods this algorithm is fast, robust and easy to understand. This is relatively efficient and gives better result when dataset are distinct. Here distinguish objects from background the background using the segmentation method. Unsupervised K-Means clustering algorithm groups the set of data Object into clusters and extracting region of interest from it.K-mean segmentation identifies separate objects within the images, and then finds regions of connected pixels with similar properties. It removes the unwanted regions by finding the boundaries between regions. Here taking three clusters, from which we want to choose the appropriate cluster [7].Here taking three clusters, which separate the three objects in the image. From the three clusters appropriate cluster choose to plot the surf algorithm and hence to extract the features. SURF Feature Extraction SURF (Speed up robust feature) method used to extract feature from the underwater fish image. The SURF-algorithm uses the same principles and steps as that of SIFT, it provide better and fast result by utilizing different scheme [8].SURF algorithm divides the scale space into levels and octaves, and the octave is further divided into uniformly spaced levels.Nonmaximal-suppression of the determinants of the hessian matrices is the heart of SURF algorithm. The use of a descriptor is to provide a unique and robust description of a feature and it can be generated based on the area surrounding an interest point. The SURF descriptor is based on Haar wavelet responses and can be calculated efficiently with integral images. It is possible to achieve rotational invariance by determining a unique orientation for an interest point. Here the surf features are plotted on the segmented fish image. Nearly 62 features are extracted by using this algorithm. SVM Classification Features extracted from the segmented fish image are fed to SVM classifier. Support vector machines (SVMs, also support vector networks) are supervised learning models. An SVM training algorithm builds a model that assigns new examples to one category or the other; making it a non-probabilistic binary linear classifier.SVMs can efficiently perform a nonlinear classification as well as linear classification. Non-linear classification can be achieved using kernel trick, which maps their inputs into high dimensional feature spaces.A SVM is a binary classifier, that is, the class labels can only take two values. Many real world problems, however, have more than two classes. To get M-class classifiers, construct set of binary classifiers each trained to separate one class from rest.Strategies for reducing the problem of multiclass classification to multiple binary classification problems. It can be categorized into One vs Rest and One vs One. Reducing

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multi-class problem to multiple binary problems termed as transformation techniques. IV. RESULTS The aim of the project is to detect the fish species. For the first step performed is Gaussian noise removal shown in Figure (1).After the noise removal the first task is to detect the fish object by removing the background obstacles.The problem felt in this phase is thatif the background obstacle is larger than the fish object it detects the obstacle as an object along with fish object.K-means algorithm stakes an important role in the segmentation process, which identifies multiple objects from image .Here taking three clusters shown in Figure(2).I opt the appropriate cluster from the three cluster and applies SURF algorithm on the fish body part shown in Figure(3).Features extracted after applying algorithm is fed to Support vector machine classifier shown in Figure(4). The following screenshots shows the result of this proposed method.

Figure1:Input image after applying noise removal and resizing

Figure 2. Three cluster images applying K- means algorithm


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Symp. Comput.Graph.ImageProcess.(SIBGRAPI), 2005, pp. 173–180. [4]Wong PohLee ,Mohd Azam Osman, Abdullah Zawawi Talib, Recognition of fish based on generalized color fourier descriptor. [5]C.Lian, S.Ruan, and T.Denœux, An evidential classifier based on feature selection and two-Step classification strategy, Pattern Recognit., vol.48,no. 7, pp. 2318–2327, 2015. [6]Wijayanti Nurul Khotimah, Agus Zainal Arifin,Anny Yuniarti,Arya Yudhi Wijaya, Dini Adni Navastara,Tuna Fish Classification using Decision Tree Algorithm and Image Processing Method, 2015 International Conference on Computer, Control, Informatics and Its Applications. [7]ConcettoSpampinato,DanielaGiordano,RobertoDiSalvo, Yun-HehChen-Burger,Robert B.Fisher, Gayathri Natarajan, Automatic Fish Classification for Underwater Species Behavior Understanding ,Proc. 1st ACM Int.Workshop ARTEMIS, 2010, pp. 45–50. [8]P M Panchal, S R Panchal2, S K Shah3 ”A Comparison of SIFT and SURF” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 2, April 2013.

Figure 3.SURF feature extraction method.

Figure4.Classified result

V.CONCLUSION Feature selection methodology can be successfully used to significantly improve the performance of fish classification systems.Classifier used for the classification provides betterclassification results. Segmentation process efiiciently segments the image into three clusters in to three clustres using k-means algorithm. For the feature extraction here used SURF algorithm,and features are trained with SVM classifier.For the future work need to classify the features with another classifiers and to evaluate the performance of SVM with the other classifiers.

References [1] M.-C. Chuang, J.-N. Hwang, andK. Williams, Supervised and unsupervised feature extraction methods for underwater fish species recognition,Proc.ICPRWorkshop Comput.Vis. Anal. Underwater Imag.(CVAUI), 2014, pp. 33–40. [2]D.-J. Lee, R.B.Schoenberger, D.Shiozawa, X.Xu,and P.Zhan,Contour matching for a fish recognition and migration-monitoring system, Proc. SPIE, vol. 5606, pp. 37–48, Dec. 2004. [3]M.S.Nery,A.M.Machado,M.F.M.Campos,F.L.C.Padua,R .Carceroni,J.P.Queiroz- Neto,Determining the appropriate feature setforfishclassificationtasks,Proc.18thBrazilian

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