K-way Min-Max Cut For Image Clustering And Junk Images Filtering From Google Images

Currently most existing image search engines such as Google Images index web im-ages majorly using text keywords extracted from the context, which may return large amount of junk information. The matching between query and context doesn't necessarily mean the matching between query and image itself, ignoring the visual content of image is the es-sential reason. Based on the search result of Google Images, we propose a novel clustering based filtering method to filter those junk images.Firstly we extract three different image features from images returned by Google, meanwhile we design appropriate kernel functions to capture the visual similarity property. Linear mixture method is used to mix all three feature kernels. Based on the kernel matrix, we apply K-way min-max cut to cluster those images into multiple clusters, and kernel weights in the mixture-of-kernel can be determined automatically. Secondly we select the best cluster in a robust way, and rank all the rest clusters according to their similarity with the best one. Finally those low-rank clusters can be filtered out as junk clusters.We obtain very comparative filtering performance throughout the experiments. In the part of low-recall level comparision, our method outperforms the current state-of-the-art in most cases, which states the robustness of clustering algorithm and best cluster selection algorithm. In the part of multi-recall level comparison, our method improves Google Images consistantly and significantly, which states the effectiveness of ranking algorithm.