Image Clustering Using Visual and Text Keywords

Abstract—In classical image classification approaches, lowlevel features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selection and distance measurement during the clustering process. In this paper, we propose an approach to generate visual keyword and combine both visual and text keywords of the image to form a multimodal vector for image classification. This multimodality helps in extracting the image to image, text to text and text to image relations. A visual keyword is derived using vector quantization of image tiles. We arrange the visual keywords in a manner analogous to the term-document matrix in information retrieval. The visual keywords when combined with text keywords result in improvement in the quality of classification. We use a recently proposed nonlinear dimensionality reduction technique, diffusion maps, to reduce the dimensionality of the image representation. Our method is evaluated on two public datasets: Lab...

• Type: Conference
• Year: 2007
• Where: CIRA
• Authors: Rajeev Agrawal, Changhua Wu, William I. Grosky, Farshad Fotouhi
• Keywords: CIRA 2007, Image, Robotics, Text Keywords, Visual Keywords