LDOP: Local Directional Order Pattern For Robust Face Retrieval

Abstract

The local descriptors have gained wide range of attention due to their enhanced discriminative abilities. It has been proved that the consideration of multi-scale local neighborhood improves the performance of the descriptor, though at the cost of increased dimension. This paper proposes a novel method to construct a local descriptor using multi-scale neighborhood by finding the local directional order among the intensity values at different scales in a particular direction. Local directional order is the multi-radius relationship factor in a particular direction. The proposed local directional order pattern (LDOP) for a particular pixel is computed by finding the relationship between the center pixel and local directional order indexes. It is required to transform the center value into the range of neighboring orders. Finally, the histogram of LDOP is computed over whole image to construct the descriptor. In contrast to the state-of-the-art descriptors, the dimension of the proposed d

Stats

• citations31
• S2 citations—
• github stars0
• HF likes0
• heat score11.29
• arxiv keydubey2018ldop

Related papers

• Local Directional Relation Pattern For Unconstrained And Robust Face Retrieval (2017)11.85
• Local Gradient Hexa Pattern: A Descriptor For Face Recognition And Retrieval (2022)13.93
• R-theta Local Neighborhood Pattern For Unconstrained Facial Image Recognition And Retrieval (2022)8.35
• Face Retrieval Using Frequency Decoded Local Descriptor (2017)10.61
• Multichannel Distributed Local Pattern For Content Based Indexing And Retrieval (2018)3.58
• Local Neighborhood Intensity Pattern: A New Texture Feature Descriptor For Image Retrieval (2017)14.11
• Learning Local Descriptors By Optimizing The Keypoint-correspondence Criterion: Applications To Face Matching, Learning From Unlabeled Videos And 3d-shape Retrieval (2016)11.75
• DCI: Discriminative And Contrast Invertible Descriptor (2018)0.00