Private Content Based Image Retrieval Shashank J, Kowshik P, Kannan Srinathan and C.V. Jawahar Is it possible for an image database to respond accurately.

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Presentation transcript:

Private Content Based Image Retrieval Shashank J, Kowshik P, Kannan Srinathan and C.V. Jawahar Is it possible for an image database to respond accurately without any knowledge of the query image. Objective: Retrieve results from an image database, while maintaining complete privacy of the query image from the database. Applications: : Medical Image Databases Surveillance Systems Logo Patent Search Defense Systems Web 2.0 Image based query retrieval Center for Visual Information Technology International Institute of Information Technology, Hyderabad, INDIA Quadratic Residuosity Assumption Consider a natural number N = p. q where p, q are large prime numbers. Construct a set `y` is called a Quadratic Residue (QR), if x | y = x 2 and x, y else `y` is called a Quadratic Non- Residue (QNR). Construct a set Y N with equal number of QRs and QNRs Quadratic Residuosity Assumption: Given a number `y` Y N, it is predictably hard to decide whether `y` is a QR or a QNR. Basic Properties: QNR x QNR = QR QNR x QR = QNR QR x QR = QR Formulation of Q i and A i ….. …. 1 … …. ….. … QR ….. …. QNR11 … …. ….. …. 1 QRQR 2 ….. …. m x n QNR 2 ….. QNR QR …. ….. …. QRQR 2 ….. …. m x n QNR 2 ….. QNR QR …. ….. …. QR ….. QNR AiAi Hierarchical Structures vary in: – Number of nodes at each level. – Information at a node. Any number of nodes can be converted into a ‘m x n’ matrix. Any information can be represented in binary format. If the user has the data about the indexing structure and the format of the information stored at a node, the algorithm can be simulated for any hierarchical structure. KD Tree – Similar to binary tree – Each node contains split dimension and split value. Vocabulary Tree – Branch factor depends on vocabulary size. – Each node contains representative visual words of its children. Extension to other Hierarchical Structures Results and Discussions KD Tree and Corel Dataset – Color Histogram (768 dimensions) – Retrieval Time: secs Vocabulary Tree and Nister Dataset – SIFT features – Vocabulary of visual words. – Retrieval Time: secs LSH and Corel Database – Used to achieve partial privacy. – 90 Hash functions with 450 bins each. – Retrieval Time: secs Related Work Blind Vision by S. Avidan and M. Butman Apply secure multi-party techniques to vision algorithms. Multi-party protocols are inefficient compared to our tailor made solution. They need privacy in both directions while PCBIR demands in one direction only. PCBIR Algorithm 1.Extract feature vector of the query image say f query. 2.The user first asks the database to send the information at the root node. 3.Using f query and the information received, the user decides whether to access the left subtree or the right subtree. 4.In order to get the data at the node to be accessed, the user frames a query Q i where i indicates the level in which the node occurs. 5.The database returns a reply A i for the query Q i. 6.The user using the data obtained from A i and f query decides which subtree to move next to. PCBIR on a Binary Search Tree Q1Q1 A1A1 Feature vector (f query ) …….. Root Info f query, f(A 1 ) Q2Q2 A2A2 f query, f(A 2 )