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Proceedings of ISOT 12 Inti Symposium on Optomechatronic Technologies Bottom Sediment Classification Method from Seabed Image for Automatic Counting System of Scallop Koichiro Enomoto Graduate School of Masashi Toda Center for Multimedia Yasuhiro Kuwahara Hokkaido Research Organization Fisheries Research Department Abashiri Fisheries Research Institute Abashiri Japan 099 3119 Email kuwahara yasuhiro hro or jp Systems Information Science Future University Hakodate Hakodate Japan 041 8655 Email g3111001 fun ac jp and Infonnation Technologies Kumamoto University Kumamoto Japan 860 8555 Email toda cc kumamoto u ac jp Abstract Each related organization conducts various fishery investigations and collects data required for estimation of re source state In the scallop culture industry in Tokoro Japan the fish resources are investigated by analyzing seabed images The seabed images are now obtainable from catamaran technology However there is no automatic technology to measure data from these images and so the current investigation technique is the manual measurement by experts The scallop is looked different from each environment Therefore a suitable algorithm to extract the scallop area depends on the bottom sediments of the seabed image In this paper we propose a method to classify the bottom sediments of the seabed image For bottom sediment classification we forge a strong classifier from weak classifiers using AdaBoost using the various texture features This paper describes a method to classify the bottom sediments presents the comparison of the effectiveness of the texture features and the results Moreover we presents the experiments results of the scallop counting based on the proposed method and evaluate the method s effectiveness I INTRODUCTION Each related organization conducts various fishery investi gations and collects data required for estimation of resource state 1 The results of investigations are used to estimate the catch size time fish are caught and future stocks Therefore investigations must more accurately measure the numbers sizes and states of fisheries Recently investigation method have been developed to measure fishes using an underwater camera or its video 2 3 However these investigations do not using automatic mea surement from underwater camera or its video T Hagisawa et al showed a method to measure the red algae for the fishery investigation of laminaria from underwater video camera 4 In the scallop culture industry in Tokoro Japan the fish resources are investigated by analyzing seabed images 1 The seabed images are now obtainable from catamaran technology However there is no automatic technology to measure data from these images and so the current investigation technique is the manual measurement by experts Therefore this in vestigation is neither efficient nor long ranging Automatic counting systems must be developed to measure the scallop more quickly and investigate fisheries more accurately The scallop habitat is the seabed of gravel and sand 5 6 and the distribution of bottom sediments include the these fields in the fishing area Tokoro 5 K Enomoto et al have already proposed the algorithms to count the scallops from the seabed image of gravel and sand because the scallop is looked different from each environments 7 8 They have already proposed the algorithms to extract the scallop areas from the seabed images of gravel and sand because the scallop is looked different from each environments Therefore a suitable algorithm depends on the bottom sediments of the seabed image In oceanography and fisheries science the methods of bottom sediment classification have been proposed from the image of side scan sonar SSS 9 12 However there is not a method to classify the bottom sediments from the seabed image In this paper we propose a method to classify the bottom sediments from the seabed image For the bottom sediment classification we forge a strong classifier from weak classifiers using AdaBoost and use the various texture features This paper describes a method to classify the bottom sediments presents the comparison of the effectiveness of the texture features and the results Moreover we present the experiment results using proposed method for the scallop of measurement The next section describes the seabed images the scallop the seabed environments and design considerations for bottom sediment classification from the seabed image We describe the various texture features and propose a method to classify the bottom sediments using AdaBoost in Sec II Section III shows the experimental method and the result gained from applying our method to the seabed images and discusses the validity of our method A Seabed Environments and Scallop Figure 1 shows the digital photographs of the seabed fields of gravel and sand 1536 x 1024 pixels and in 24 bit color 978 1 4673 2877 7112 31 00 2012 IEEE These seabed images contain scallops gravel dead shells and so on In Fig l a the gravels are a variety of sizes shapes and colors In Fig 1 b the grain size of sand is smaller than gravel There are very few the gravel something of chromatic color in the sand field Figure 2 shows the scallop areas of 64 x 64 pixels in the fields of gravel and sand In the gravel field the scallop areas have special features such as being sepia or sienna in color and shaped like fans with a striped pattern Fig 2 a and b The scallop cannot hide under the gravel because the gravel is big and heavy for its In the sand field the scallop areas have special features such as the shelly rims being white and shaped like fans Fig 2 c and d There is no sand in the shelly rim but the scallop shell is covered with sand The scallop opens and closes its shell while it is alive and breathing For the same reason the scallop does not overlap with other scallops in the fields of gravel and sand These facts are based on the professional knowledge of ecologists and fishennen A suitable algorithm shold depend on the bottom sediments of the seabed image K Enomoto have already proposed the algorithms to extract the scallop areas from the seabed images of gravel and sand and shown the method s effectiveness 7 8 because the scallop is looked different from each environments These extraction rate accuracy are 95 0 7 and 89 0 8 in the scallop extraction algorithm for the fields of gravel and sand Therefore it is need to select a suitable scallop extraction algorithm on the basis of each field and develop the bottom sediment classification method from the seabed image For oceanography fisheries science and fishery industry it is important in the bottom sediment classification The results of bottom sediment classification is used to estimate the sources of natural resources tide and fishing place and important for fisheries management The fish seaweed and fishshell are changed by the bottom sediment in ecology 5 Moreover the benthic and the bottom sediment influence each other The methods of bottom sediment classification have been proposed from the image of side scan sonar SSS 9 12 These methods classify the bottom sediment using the texture analysis of the image of side scan sonar L Atallah and P J P Smith showed a method of the bottom sediment classification using the texture features and K nearest neighbor 9 H Lan naya et al showed also a method using the texture features and support vector machine 12 These methods can not apply the bottom sediment classification of the seabed images because the seabed image has the different information from the images of side scan sonar The seabed images are color and high space resolution more than the images of the side scan sonar Therefore the texture features require to be selected for the bottom sediment classification to measure the scallop from the seabed image a b Fig I Seabed images a Gravel field b Sand field a b c d Fig 2 The images of scallop area size 64 x 64 The gravel seabed images are a and b The sand seabed images are c and d B Proposed Method Figure 3 shows our proposed system to extract the scallop area First the object image is performed the preprocessing 8 Next the seabed environment of the object image is identified by the bottom sediment classification The scallop areas are extracted using a suitable scallop extraction algorithm based on the result of the bottom sediment classification Finally the counting results show the user In this paper we propose a method to classify the bottom sediments of the seabed image We forge a strong classifier from weak classifiers using AdaBoost and use the various texture features such as the histogram GLCM and GLDM Section II explains a method Section III shows also the results gained from applying our method to the seabed images and Input image Preprocessing I Smoothing I I Extract recognizable area I i t Remove frame area J Bottom sediments classification I Identify using AdaBoost I Gravel fiel andfield I Extraction algorithm II Extraction algorithm I for the gravel field for the sand field Result image Fig 3 Proposed counting system J the results of the scallop extraction based on the bottom sediment classification II BOTTO M SEDI MENT CLASSIFICATION A AdaBoost A suitable algorithm to extract the scallop area should depend on the bottom sediments of the seabed image In this paper the bottom sediments are separated the fields of gravel and sand We describe a method to classify the bottom sediments of the seabed image using AdaBoost 13 AdaBoost is a typical instance of Boosting learning Ad aBoost learning algorithm is used to boost the classification performance of a simple learning algorithm It does this by combining a collection of weak classification functions to form a stronger classifier In the language of boosting the simple learning algorithm is called a weak leaner After the first round of learning the examples are re weighted in order to emphasize those which were incorrectly classified by the previous weak classifier The final strong classifier takes the form of a perceptron a weighted combination of weak classifiers followed by a threshold The AdaBoost algorithm is shown in Fig 4 In this paper the examples images xi Yi are clipped the seabed images If the image Xi is the gravel field Yi 1 and if the image Xi is the sand field Yi 1 In strong classifier H x if the value is positive the input image X is the gravel field and if the value is negative it is the sand field We used the texture features such as the histogram the gray level co occurrence matrix GLCM and the gray level difference matrix GLDM These features are calculated the each color elements in color space of RGB and HLS The texture features are described in the following section B Weak Learner 1 Color Histogram There are the various colorful gravels in the gravel field but there are the gray sands and the white shell debris Fig 1 We define the color features as the histogram In this paper we used these statistics of the histogram such as Mean Standard Deviation Contrast These features are calculated the each color elements in color space of RGB and HLS The weak leaner h x for the statistics of the histogram Feolor are defined as h x 1 1 if Peolor T otherwise 1 Here T are the thresholds for the each color features If a statistics Feolor is more than a threshold T the image is the gravel field otherwise this is the sand field 2 GLCM The gray level co occurrence matrix GLCM 14 15 is a popular statistical method of extracting tex tural features from images The values of the co occurrence matrix elements present relative frequencies with which two neighboring pixels where one of them has gray level i and other j Such matrix is symmetric and also a function of the angular relationship between two neighboring pixels The GLCM is computed over a segment which is inspected by a displacement vector 6 defined by its amplitude r and its orientation e Fig 5 In this paper we used these statistics of the GLCM such as Mean Standard Deviation ASM Angular Second Moment Energy Entropy Correlation Homogeneity Dissimilarity Contrast Here we set r I 3 5 and e 0 The weak leaner h x for the statistics of the GLCM FCLCM are defined as h x 1 1 if PCLCM T otherwise 2 Here T are the thresholds for the each features If a statistics FCLCM is more than a threshold T the image is the gravel field otherwise this is the sand field 3 GLDM The gray level difference matrix GLDM 15 16 is a statistical method of extracting textural features like GLCM The values of the difference matrix elements present relative frequencies of difference with which two neighboring pixels where one of them has gray level i and other j A displacement vector is the same as 6 of the GLCM Give Xl Yl xm Ym where Xi E X Yi E Y 1 1 Initialize Dl i For i 1 T Train weak learner using distribution Dt Get weak hypothesis ht X I I with error tt Pr i D ht Xi i Yi Choose at ln l tE Update Dt i x e at if ht Xi Yi Zt eat if ht Xi i Yi Dt i exp atYiht Xi Zt where Zt is a normalization factor chosen so that Dt 1 will be a distribution Output the final hypothesis T H x sign L atht x t l Fig 4 AdaBoost algorithm In this paper we used these statistics of the GLDM such as Mean SD Standard Deviation ASM Angular Second Moment IDM Inverse Difference Moment Entropy Contrast Here we set r I 3 5 and e 0 The weak leaner h x for the statistics of the GLDM FCLDM are defined as h x 1 1 if PCLDM otherwise 3 Here are the thresholds for the each features If a statistics FCLDM is more than a threshold the image is the gravel field otherwise this is the sand field III EXPERI MENTS In this section we will evaluate the accuracy of the bottom sediment classification and apply the proposed method to extract the scallop area A Method 1 Experiment 1 Recognition Accuracy Evaluation In the weak leaner we use the combination of the 7 data sets The data sets are Data I Histogram Data 2GLCM Data 3GLDM Data 4Histogram and GLCM Data 5Histogram and GLDM X y Gray value j x 1 Y 1 Gray value j Fig 5 Definition of relative locations of two pixels Data 6GLCM and GLDM Data 7 Histogram GLCM and GLDM We examine the accuracy of the bottom sediment classifica tion using the leave one out cross validation LOOCV Leave one out cross validation involves using a single observation from the original sample as the validation data and the remaining observations as the training data This is repeated such that each observation in the sample is used once as the validation data Each data sets are evaluated the accuracy of the bottom sediment classification using LOOCV We used 240 gravel images and 149 sand images in this experiment These texture images were selected the upper left of the middle of the frame that have a margin 64 x 64 pixels and its size is 128 x 128 pixels 8 Figure 6 shows the original samples in the fields of gravel and sand 2 Experiment 2 Scallop Extraction We examine the scallop extraction in combination with the proposed method In the gravel field the scallops and the other areas are extracted by the scallop extraction algorithm for the sand field The opposite is also true We tried to extract the scallop areas using the result of the bottom sediment classification a b c d Fig 6 The samples of a original samples size 128 x 128 The gravel seabed images are a and b The sand seabed images are c and d a b Fig 7 The samples of a scallop image size 128 x 128 The gravel seabed image is a The sand seabed image is b In the gravel field the scallop areas are extracted by using following processes 7 First the candidate scallop areas were extracted only using only shape features the strong edge points Some candidate areas were obtained in this process The scallop area is selected from the candidate areas using the color feature or the striped pattern feature In the sand field the scallop areas are extracted by using following processes 8 The candidate shelly rim pixels are extracted from the obtained image by dynamic threshold processing Next the candidate scallop areas are extracted using the Hough transform Finally the scallop areas are extracted by threshold processing using the shell feature and the shelly rim feature Here the image with the scallop area is referred to a scallop image and the image without this is referred to an other image We used 24 scallop images and 87 other images in the gravel field and 20 scallop images and 87 other scallop images in the sand field Here we used only the scallop images that all scallops are extracted by the suitable extraction algorithm on the each fields and the Data 7 for the bottom sediment classification Figure 7 shows the images of scallop and other in the fields of gravel and sand B Results 1 Results of Experiment 1 Table I presents the experi ment results of the recognition rate of the bottom sediment classification All recognition rate is more than 96 The recognition rates of the gravel and the sand are 100 0 and 96 6 in Data 6 and 7 The samples of the bottom sediment classification are presented in Fig 8 In Fig 8 a c these images were recognized incorrectly only a feature such as histogram GLCM and GLDM but its were recognized correctly using these features and AdaBoost 2 Results of Experiment 2 Table II and III present the experiment results of the scallop extraction using the scallop extraction algorithm for the fields of gravel and sand All images are recognized correctly in Table II and III In the TABLE I THE RECOGNITION RATE OF THE BOTTOM SEDIMENT CLASSIFICATION Recognition rate Gravel Sand Total Data I 98 3 96 0 97 43 Data 2 99 6 97 3 98 7 Data 3 98 8 96 0 97 7 Data 4 99 2 97 3 98 5 Data 5 99 2 96 6 98 2 Data 6 100 0 96 6 98 7 Data 7 100 0 96 6 98 7 Fig 8 The sample images of the bottom sediment classification The gravel seabed images are a and b The sand seabed image is c These images were not recognized correctly only a texture features but there were recognized using Data 6 and 7 gravel field Some images were extracted the areas using the scallop extraction algorithm for the sand field The examp