978-1-5386-9346-9/19/$31.00 ©2019 IEEE Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques Maher Alrahhal 1 , Supreethi K.P. 2 1,2 CSE Dept., JNTUH Collage of Engineering, Hyderabad 500085, Telangana, India 1 maherrahal92@gmail.com, 2 supreethi.pujari@gmail.com Abstract: CBIR is a very important domain, especially in the last decade due to the increased need for image retrieval from the multimedia database. In general, we extract low level (color, texture, and shape) or high-level features (when we include machine learning techniques) from the images. In our work, we proposed a new CBIR system using Local Neighbor Pattern (LNP) with supervised machine learning techniques. We evaluated the performance of this system by comparing the system with Local Tetra Pattern (LTrP) using Corel 1k, Vistex and TDF face databases. We used three types of the database (i.e color, texture and face databases) to improve the effectiveness of our system. Performance analysis shows that LNP gives better performance regarding the average recall than LBP, LDP, and LTrP. To increase the accuracy of this system we used the LNP method with machine learning techniques and performance analysis shows that local pattern with machine learning techniques improves the average accuracy from 36.23% to 85.60% when we use LNP with cubic SVM on DB1 (Corel1K), and from 82.51% to 99.50 % when we use LNP with fine KNN on DB2 (Vistex DB), and from 56.63% to 95% when we use LNP with ensemble subspace discriminant on DB3 (face DB). Keywords: Content-Based Image Retrieval, Local Neighbor Patterns, Supervised Machine Learning Techniques, Classification, MATLAB. I. INTRODUCTION Image Retrieval (IR) means the process of searching the related images in images Database, and retrieve the most similarity image to the user. IR techniques can be classified into Text-Based or Content-Based Image Retrieval. TBIR is the process of manually adding annotation or description to the image in the database to describe the content of images and sometimes for describing other metadata of images like size, dimensions, and format of images. The advantages of the TBIR system are simple and very fast in displaying the result and depend on matching a textual query with description of the images. However, this method has many disadvantages, such as the error rate is high and a great amount of manpower and material resources are needed [1]. Also, adding a description of the image depend on our points of view, or how we understand the image, so the description of the image may differ from one person to another. Finally, the big issue is adding an annotation to the image depending on your languages. CBIR is a method that is used to find an image from a set of the large database as per the user requirement. CBIR is also known as query by image content (QBIC) [2]. CBIR ([3], [4], [5], [6], [7]) is an alternative to the traditional TBIR which overcomes the above limitations. CBIR covers several areas like image segmentation, extracts a feature from the image and converts this feature into a semantic feature. In general, we extract low level (color, texture, and shape) or high-level features (when we include machine learning techniques) from the images [8]. In general, CBIR consists of two phases [9] off-line phase for feature extraction and online phase for image retrieval. In the offline phase the system extracts the feature from all the images in the DB and store them in DB. In on-line phase, the user inputs a query and the system extracts the features from this image and measure the similarity by calculate the distance between query image (i.e feature vector) and all images in DB (i.e feature vectors), then sort the distance in ascending way and retrieve the top k images to the user. Another technique in image retrieval is combining text- based and content- based to increase accuracy and get better performance. CBIR has been used in several fields, such as satellite images [10], remote sensing, medical imaging[11], fingerprints scanning ([12], [13]) and biodiversity information systems. CBIR techniques are being used in the area of satellite images to find earth minerals, aerial survey, for monitoring agriculture, to generate weather reports and for tracking surface objects. Medical imaging is one of the prominent areas of application of CBIR which can be used for monitoring patient health reports, to aid diagnosis by identifying the similar past cases etc. When given a fingerprint query image CBIR systems can be used to extract the similar fingerprint images that results in verification of an individual. Fingerprints are used in banking sector,colleges, corporate companies, and forensic labs . II. RELATED WORK For many years, researchers have been using different local descriptor methods for image retrieval and classification. Image classification is supervised machine learning, for that in training step, we have data sample with a label (class name) for