Visual Recognition Using Local Quantized Patterns^*

Sibt ul Hussain¹ and Bill Triggs²

¹GREYC, CNRS UMR 6072, Université de Caen, France
sibt.ul.hussain@gmail.com

²Laboratoire Jean Kuntzmann, Grenoble, France
bill.triggs@imag.fr

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 object 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 image content. Current local pattern features use hand-specified codings that are limited to small spatial supports and coarse graylevel comparisons. 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 features including HOG, LBP and LTP and their combinations on a range of challenging object detection and texture classification problems.

^*This work was supported by the Higher Education Commission (HEC) of Pakistan and European Commission research project CLASS.

LNCS 7573, p. 716 ff.

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