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Title: Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval
Authors: Banerjee P.
Bhunia A.K.
Bhattacharyya A.
Pratim Roy, Partha
Murala S.
Published in: Expert Systems with Applications
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 significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors (Ii) only, rather we take into account the sign of difference values between Ii and its adjacent neighbors along with the central pixels and same set of neighbors of Ii. This makes our pattern more resistant to illumination changes. Moreover, most of the local patterns including LBP concentrates mainly on the sign information and thus ignores the magnitude. The magnitude information which plays an auxiliary role to supply complementary information of texture descriptor, is integrated in our approach by considering the mean of absolute deviation about each pixel Ii from its adjacent neighbors. 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. Finally, the sign pattern (LNIPS) and the magnitude pattern (LNIPM) are concatenated into a single feature descriptor to generate a more effective 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. © 2018 Elsevier Ltd
Citation: Expert Systems with Applications (2018), 113(): 100-115
Issue Date: 2018
Publisher: Elsevier Ltd
Keywords: Feature extraction
Local Binary Pattern
Local Neighborhood Intensity Pattern
Texture feature
ISSN: 9574174
Author Scopus IDs: 57202820553
Author Affiliations: Banerjee, P., Department of CSE, Institute of Engineering & Management, Kolkata, India
Bhunia, A.K., Department of ESE, Institute of Engineering & Management, Kolkata, India
Bhattacharyya, A., Department of CSE, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Roy, P.P., Department of EE, Indian Institute of Technology Ropar, India
Murala, S., Department of EE, Indian Institute of Technology Ropar, India
Corresponding Author: Roy, P.P.; Department of EE, Indian Institute of Technology RoparIndia; email:
Appears in Collections:Journal Publications [CS]

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