Near duplicate detection of images with area and proposed pixel-based feature extraction

Efficient and accurate near-duplicate recognition is the trendy research area. Identification of invalid near-duplicate images offers a wide range of applications, including digital picture forensics, web-scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area-based features and pixel-based features are extracted. Here, the area-based feature extraction includes the contrast context histogram (CCH-descriptors) and improved weighted bag of visual word (w-BovW) features; the pixel-based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner.