Abstract

Features such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) have been very successful in a number of areas including texture analysis, face recognition and ob ject detection. They are based on the idea that small patterns of qualitative local gray-level differences contain a great deal of information about higher-level im- age content. Current local pattern features use hand-specified codings that are limited to small spatial supports and coarse graylevel compar- isons. We introduce Local Quantized Patterns (LQP), a generalization that uses lookup-table-based vector quantization to code larger or deeper patterns. LQP inherits some of the flexibility and power of visual word representations without sacrificing the run-time speed and simplicity of local pattern ones. We show that it outperforms well-established fea- tures including HOG, LBP and LTP and their combinations on a range of challenging ob ject detection and texture classification problems.