Proceedings of the 2004 ACM symposium on Applied computing
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
W2Go: a travel guidance system by automatic landmark ranking
Proceedings of the international conference on Multimedia
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Beyond search: Event-driven summarization for web videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
IntentSearch: Capturing User Intention for One-Click Internet Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE MultiMedia
Hi-index | 0.00 |
This paper considers the problem of selecting representative images to summarize the original dataset. People search images by a keyword in traditional image retrieval system. However, the result shows a lack of diversity in semantic theme. For the problem, we propose a new method of representative image selection. We try to divide images into different categories from the semantic point of view and select canonical images based on an image clustering method. First, we use mutual nearest neighbor consistency to adjust the similarity between feature vectors as the input for the AP clustering. Then we select representative clusters based on cluster ranking and finally take the images of the cluster center from representative clusters as a summary of the image dataset. We evaluate our approach on the image dataset from Google with ten categories. The experiment results showed that the selected images can summarize the content of the original image dataset intuitively and effectively. And the selected images are diverse in semantic meaning as well.