Full Direction Local Neighbors Pattern (FDLNP)

Abstract

In this paper, we proposed the Full Direction Local Neighbor Pattern (FDLNP) algorithm, which is a novel method for Content-Based Image Retrieval. FDLNP consists of many steps, starting from generating Max and Min Quantizers followed by building two matrix types (the Eight Neighbors Euclidean Decimal Coding matrix, and Full Direction Matrixes). After that, we extracted Gray-Level Co-occurrence Matrix (GLCM) from those matrixes to derive the important features from each GLCM matrixes and finishing with merging the output of previous steps with Local Neighbor Patterns (LNP) histogram. For decreasing the feature vector length, we proposed five extension methods from FDLNP by choosing the specific direction matrixes. Our results demonstrate the effectiveness of our proposed algorithm on color and texture databases, comparing with recent works, with regard to the Precision, Recall, mean Average Precision (mAP), and Average Retrieval Rate (ARR). For enhancing the image retrieval accuracy, we proposed a novel framework that combined the image retrieval system with clustering and classification algorithms. Moreover, we proposed a distributed model that used our FDLNP method with Hadoop to get the ability to process a huge number of images in a reasonable time.