Abstract
As tools and systems for producing and disseminating image data have improved significantly in recent years, the volume of digital images has grown rapidly. An efficient mechanism for managing such images in a digital archive system is therefore needed. In this study, we propose an image classification technique that meets this need. The technique can be employed to annotate and verify image categories when gathering images. The proposed method segments each image into non-overlapping blocks from which color and texture features can be extracted. Support Vector Machine (SVM) classifiers are then applied to train and classify the images. Our experimental results show that the proposed classification mechanism is feasible for digital archive management systems.
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Li, CH., Chiu, CY., Wang, HA. (2005). Image Classification for Digital Archive Management. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds) Digital Libraries: Implementing Strategies and Sharing Experiences. ICADL 2005. Lecture Notes in Computer Science, vol 3815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599517_10
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DOI: https://doi.org/10.1007/11599517_10
Publisher Name: Springer, Berlin, Heidelberg
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