Using Redundant Bit Vectors for Near-Duplicate Image Detection

Images are amongst the most widely proliferated form of digital information due to affordable imaging technologies and the Web. In such an environment, the use of digital watermarking for image copyright infringement detection is a challenge. For such tasks, near-duplicate image detection is increasingly attractive due to its ability of automated content analysis; moreover, the application domain also extends to data management. The application of PCA-SIFT features and LocalitySensitive Hashing (LSH) — for indexing and retrieval — has been shown to be highly effective for this task. In this work, we prune the number of PCA-SIFT features and introduce a modified Redundant Bit Vector (RBV) index. This is the first application of the RBV index that shows near-perfect effectiveness. Using the best parameters of our RBV approach, we observe an average recall and precision of 91% and 98%, respectively, with query response time of under 10 seconds on a collection of 20, 000 images. C...

• Type: Conference
• Year: 2007
• Where: DASFAA
• Authors: Jun Jie Foo, Ranjan Sinha
• Keywords: DASFAA 2007, Database, PCA-SIFT Features, Query Response Times, RBV Index