Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval

TL;DR

This paper introduces the Local Neighborhood Intensity Pattern (LNIP), a new texture descriptor for image retrieval that considers adjacent neighbors' intensity differences, leading to improved retrieval accuracy across multiple databases.

Contribution

The paper proposes LNIP, a novel texture feature descriptor that incorporates adjacent neighbor information for better image retrieval performance.

Findings

LNIP outperforms existing local pattern methods in retrieval accuracy.

The method achieves higher precision and recall on multiple texture and face image databases.

Significant improvements are demonstrated across four diverse datasets.

Abstract

In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3*3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold a significant amount of texture information that can be considered for efficient texture representation for CBIR. Taking this into account, we develop a new texture descriptor, named as Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Since sign and magnitude patterns hold complementary information to each other, these two patterns are concatenated into a single feature descriptor to generate a more concrete and useful feature descriptor. The proposed descriptor has been tested for image retrieval on four databases, including three texture image databases - Brodatz texture image database, MIT VisTex database and Salzburg texture database and one face database AT&T face database. The precision and recall values observed on these databases are compared with some state-of-art local patterns. The proposed method showed a significant improvement over many other existing methods.