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Integration of Visual and Text-Based Approaches for the Content Labeling and Classification of Photographs

McKeown, Kathleen; Hatzivassiloglou, Vasileios; Paek, Seungyup; Sable, Carl L.; Jaimes, Alexjandro; Schiffman, Barry H.; Chang, Shih-Fu

Annotating photographs automatically with content descriptions facilitates organization, storage, and search over visual information. We present an integrated approach for scene classification that combines image-based and text-based approaches. On the text side, we use the text accompanying an image in a novel TF*IDF vector-based approach to classification. On the image side, we present a novel OF*IIF (object frequency) vector-based approach to classification. Objects are defined by clustering of segmented regions of training images. The image based OF*IIF approach is synergistic with the text based TF*IDF approach. By integrating the TF*IDF approach and the OF*IIF approach, we achieved a classification accuracy of 86%. This is an improvement of approximately 12% over existing image classifiers, an improvement of approximately 3% over the TF*IDF image classifier based on textual information, and an improvement of approximately 4% over the OF*IIF image classifier based on visual information.

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Academic Units
Computer Science
Publisher
CM SIGIR'99 Workshop on Multimedia Indexing and Retrieval
Published Here
May 3, 2013