Exploring features in a Bayesian framework for material recognition

By Ce Liu, Lavanya Sharan, Edward H. Adelson, Ruth Rosenholtz. 2010

267 citations recorded by the source corpus.

We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computer vision, it is difficult to find good, reliable features that can tell material categories apart. Our strategy is to use a rich set of low and mid-level features that capture various aspects of material appearance. We propose an augmented Latent Dirichlet Allocation (aLDA) model to combine these features under a Bayesian generative framework and learn an optimal combination of features. Experimental results show that our system performs material recognition reasonably well on a challenging material database, outperforming state-of-the-art material/texture recognition systems.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Unified Perceptual Parsing for Scene Understanding (2018, background): “-sofais made of fabric. classiﬁcation [1] and scene recognition [2]; objects and stuﬀ are parsed and segmented precisely at pixel-level [3,2]; Texture and material perception and recognition have been studied in [4] and [5].”
Material recognition in the wild with the Materials in Context Database (2014, methodology): “FMD has been used in research on new features and learning methods for material perception and recognition [17, 10, 20, 25].”
Learning Hybrid Image Templates (HIT) by Information Projection (2012, background): “In recent years, by combining different types of features in a discriminative framework [25], [26], [27], [28], [29], [30], [31], [32], much progress has been made toward improving the accuracy of object categorization.”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, methodology): “[48] extended [47] by using a kernel SVM and reported that SVM outperforms the aLDA classifier significantly.”
Recognizing Materials Using Perceptually Inspired Features (2013, methodology): “In an early version of this work (Liu et al. 2010), we had used only the LDA-based classifier, and we had reported an accuracy of 44.”
Selective Search for Object Recognition (2013, unknown): “We represent texture using fast SIFT-like measurements as SIFT itself works well for material recognition [20].”
Classification of Trash for Recyclability Status (2016, methodology): “Another more image based classification of materials was performed on the Flickr Materials Database [5].”
Robotic Tactile Perception of Object Properties: A Review (2017, background): “a popular approach to recognize object material [48]–[50].”