Feature Extraction using GLCM for Dietary Assessment Application Akshada A. Gade, Arati J. Vyavahare P.E.S’s Modern College of Engineering Pune, Maharashtra, India Abstract This paper offers technique for dietary assessment towards mechanically detect the type of food from various pictures captured during eating occasions. Recognition of food is complicated procedure since most of the food items are varies in shape and appearance. To achieve this task segmentation is important for labelling of food. The features of each segmented regions are extracted by capturing visual content of image. System works well on the most relevant six statistical parameters or texture features computed by using Gray Level Co-occurrence Matrix (GLCM). Then construct a feature vector to represents all feature values. The operation of classification will be performed on the basis of defined features. Experimental results on various food items are obtained. This food recognition system can be easily integrated into dietary assessment applications. By analysing food portion and size information, system will also calculate calories and nutrition values. For obtaining better performance and accuracy in food recognition, system needs to extract multiple features. 1. Introduction Dietary intake offers significant insights for organizing interference programs for prevention of sickness and disease. As there is a rising worry with regard to unending diseases or several health problems interrelated to the diet together with obesity or cancer so one needs to do correct judgment of foodstuff and beverage consumption. It is essential to promote individuals to be healthy and maintain great lifestyles by participating in the control of long-time period fitness selections. Determine accurate dietetic intake is considered to be a difficult challenge in the health and nutrition fields [8]. Traditional dietary assessment is comprised of written and orally mention strategies which is tedious and time consuming, also requires nutrition expert for everyday monitoring. This project builds an approach to locate and recognize perceptually similar food objects with the help of single food image where every food item is identified with the help of segmentation and image features for dietary assessment applications. The research also focuses on providing correct number of calories and nutrient intake. This method makes use of the image analysis tools for dietary assessment application. 2. Literature survey Dietary assessment has been a trendy topic in biomedical and health associated fields for years. In case of computer vision, food identification is a category of recognition. Fengqing Zhu [1] proposed a Multiple Hypothesis Segmentation and Classification (MHSC) system where food identification is accomplished by integrating features such as texture, color and SIFT descriptors. These features combined to form single feature vector and classification is done by using Support Vector Machine (SVM) which provides 65% accuracy on Japanese food databases. Edward J. Delp and carol J. Boushey [2] described new approach for food identification using combination of 8 local features and 4 global features for accurate visual description of food item. A “voting” based late decision fusion classifier used to identify the food items and by using feature channel one can improve the classification rate more than 7%. Marios M. Anthimopoulos [3] develops a food recognition system used to estimate meals carbohydrate content especially for diabetic patients which is based on bag-of-feature (BoG) model. Features are extracted with the help of SIFT descriptor or color descriptor. The results say that the SIFT- based descriptors are comparatively less sensitive to color shifts and intensity changes so their performance is much better than the color descriptors. In 2016, Ju-Chin Chen [4] proposed a system based on nutrition composition analysis by using local orientation descriptor, where the features of the food deal with relative to variations in scale, texture, rotation, and deformation. Local Orientation Descriptor (LOD) and colour features such as colour moment and histograms in YIQ colour space are incorporated for classification. For nutrition classification SVM with RBF kernel is applied. For quantity estimation coin used as a reference object. For the Japanese food database combination of color features and LBP system gives food recognition rate of 82.8%, combination of color and texture features gives recognition rate up to 85.6% and color features with LOD system gives highest food recognition rate as 87.9%. Shijin Kumar and Dharun [5] shows the extraction of nine texture features using GLCM and three shape Features such as perimeter, area, circularity using Connected Regions for diagnosis of brain abnormalities, where they conclude that the International Journal Multimedia and Image Processing (IJMIP), Volume 8, Issue 2, June 2018 Copyright © 2018, Infonomics Society 409