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This paper proposes a novel face representation based on Local Quantized Patterns (LQP). LQP is a generalization of local pattern features that makes use of vector quan-tization and lookup table to let local pattern features have many more pixels and/or quantization levels without sacrificing simplicity and computational efficiency. Our new LQP face(More)
We present a family of sliding window object detectors that combine a rich visual feature set with methodological advances from [2, 4, 5], giving state-of-the-art performance on several important datasets. Using the Latent SVM learning framework [4], our basic root detectors outperform the single component part-based ones of Felzenszwalb et. al on 9 of 10(More)
This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been shown recently that small local pixel pattern distributions can be highly discrimina-tive.(More)
The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction(More)
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