A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

Abstract

<span>Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods.</span>

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

“…In this proposed work, the underwater image enhance benchmark (UIEB) images [19] are taken for quantitative experimental analysis. In this research considered five UIEB images and all these images are enhanced with our proposed method and then compared their performances with various existing methods [20]- [24] using above mentioned performance metrics. Table 1 describes the average values of the proposed and existing algorithms for various performance metrics like UIQM, UCIQE, PCQI, average gradient, edge intensity and entropy for UIEB images of proposed and existing algorithm [25], [26].…”

ISO/IEC 25010 based evaluation of rice seed analyzer: a machine vision application using image processing technique

“…During the training, the model is taught to recognize images in various specific colors and whether the pattern received is accepted for hashing analysis and evaluation. Hashing is a popular [12] image retrieval technique. The supervised hashing method used in this data aim to improve hashing quality [13] by incorporating semantic labels into the learning process.…”