Material classification and automatic content enrichment of images using supervised learning and knowledge bases

Sri Abhishikth Mallepudi; Ricardo A. Calix; Gerald M. Knapp

doi:10.1117/12.876583

11 February 2011 Material classification and automatic content enrichment of images using supervised learning and knowledge bases

Sri Abhishikth Mallepudi, Ricardo A. Calix, Gerald M. Knapp

Author Affiliations +

Proceedings Volume 7881, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V; 788113 (2011) https://doi.org/10.1117/12.876583
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

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Sri Abhishikth Mallepudi, Ricardo A. Calix, and Gerald M. Knapp "Material classification and automatic content enrichment of images using supervised learning and knowledge bases", Proc. SPIE 7881, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V, 788113 (11 February 2011); https://doi.org/10.1117/12.876583

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