Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval

By Subrahmanyam Murala, Rudra Prakash Maheshwari, Balasubramanian Raman. IEEE Transactions on Image Processing, 2012

706 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dac55e245b4da3d10f8a819de18c17dce9b968e8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Li Liu
2. Paul W. Fieguth
3. Xiaogang Wang
4. Matti Pietikäinen
5. Shiv Ram Dubey
6. Yulan Guo
7. Luc Van Gool
8. Xiaoou Tang

Citations in the corpus

• A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, methodology): “The feature descriptor representation utilizes the visual cues of the images selected manually based on the need [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].”
• Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data (2018, methodology): “Furthermore, we compared our method with three typical methods: local binary pattern (LBP) [40], local texture pattern (LTP) [41], and local tetra patterns (LTrPs) [42], which consider flat feature vectors by using different feature extraction methods.”
• Local binary features for texture classification: Taxonomy and experimental study (2017, background): “Local Tetra Patterns LTrP Image Retrieval 2012 [130]”