An unsupervised method for clustering images based on their salient regions of interest

We have developed a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. In the implemented model cluster membership is assigned based on feature vectors extracted from salient ROIs. This paper focuses on the experimental evaluation of the proposed approach for several combinations of feature extraction techniques and unsupervised clustering algorithms. The results reported here show that this is a valid approach and encourage further research. Categories and Subject Descriptors I.4.8 [Image Processing and Computer Vision]: Scene Analysis General Terms Algorithms, Human Factors, Performance. Keywords Visual Attention, Image Retrieval, Clustering.

• Type: Conference
• Year: 2006
• Where: MM
• Authors: Gustavo B. Borba, Humberto R. Gamba, Oge Marques, Liam M. Mayron
• Keywords: Implemented Model Cluster, MM 2006, Salient Regions, Unsupervised Clustering Algorithms