Material classification and automatic content enrichment of images using supervised learning and knowledge bases
Document Type
Conference Proceeding
Publication Date
3-29-2011
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.
Publication Source (Journal or Book title)
Proceedings of SPIE - The International Society for Optical Engineering
Recommended Citation
Mallepudi, S., Calix, R., & Knapp, G. (2011). Material classification and automatic content enrichment of images using supervised learning and knowledge bases. Proceedings of SPIE - The International Society for Optical Engineering, 7881 https://doi.org/10.1117/12.876583
