1. Presented by Arshad Jamal, Rajesh Dhania, Vinkal Vishnoi Active hashing and its application to image and text retrieval Yi Zhen, Dit-Yan Yeung, Published in DMKD Feb 2012

2. Introduction Computing similarity plays a fundamental role Hashing based methods gained popularity for large- scale similarity search Hashing based Tree based Suitable for low dimensions Data Dependent Data Independent Unsupervised Semi- supervised This paper proposes a novel Framework for Active Hashing

3. Related work Locality Sensitive Hashing Goal is to assign similar binary code for data points that are closer in feature space [Random Linear Projection + Thresh] Code length could become quite large Spectral Hashing Performs spectral decomposition to learn hash functions Assumes data to be uniformly distributed Active Learning Identify and present the most informative unlabeled data to human experts for labeling

4. Related Work: Semi-supervised Hashing Given N normalized data points of D dimensions Learn K Hash functions to generate K-bit binary code Build two set of point pairs S (Similar), D(Dissimilar) Together they characterize the semantic similarity Hash functions are learned by maximizing an objective function,

5. Limitations of SSH Point pairs from both S and D sets are considered to be equally important For multi-class data, the D points picked from closer or farther class contribute same weight More dissimilar points will spoil the learned hash function C1 C2 C3

6. Active Hashing (Greedy AH) Tries to overcome the limitations of SSH by picking most informative points Algorithm: Three main steps Given (L, U) labeled and un-labeled data points and candidate set C Select most informative pts A from C Get A labeled by an expert Update L, U, C Train the hash functions based on L & U

7. Greedy AH: Selecting data points Based on SSH model hash function Intuitively, the term indicates the certainty of x Data certainty (DC): Data points with smallest f will be the most informative points

8. Batch mode Active Hashing Selecting points one by one is inefficient and suboptimal Set of points are selected and processed to learn a Hash fn. µ is indicator function deciding about the presence of a point f is a vector of normalized certainty values in C K is positive semi-definite similarity matrix defined on C Choose M examples with largest µ

9. Experimental evaluation-I Image retrieval (MNIST dataset): Results reported for different parameter settings Text Retrieval (20Newsgroups (NEWS) data set) Random vs BMAH: Performance improvement

10. Experimental evaluation-II Image retrieval (MNIST dataset) BMAH vs GAH: BMAH takes less time

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