Abnormal event detection is an important issue in video surveillance applications. The goal is to detect abnormal or suspicious behaviors while given training samples that contain only normal behaviors. Sparse representation has showed its effectiveness in abnormal event detection [2,3,4,5], where a dictionary is commonly learned during training and anomalies are detected based on reconstruction error from the learned dictionary. Note that only a small proportion of the data is used for trainingrelatively to the huge amount of surveillance data, there is a high risk of incomplete normal patterns in the training data. Consequently, dictionary learning is crucial to the overall abnormality detection performance.We propose a Behavior-Specific Dictionary (BSD) algorithm, which takes into consideration the relation of atoms in one dictionary without 1,a1,2,...a1,d1,

Discovering common visual patterns (CVPs) between two images is a challenging problem, due to the significant photometric and geometric transformations, and the high computational cost. In this paper, we formulate CVPs discovery as a graph matching problem, depending on pairwise geometric compatibility between feature correspondences. To efficiently find all CVPs, we propose two algorithms--Preliminary Initialization Optimization (PIO) and Post Agglomerative Combining (PAC). PIO reduces the search space of CVPs discovery based on the internal homogeneity of CVPs, while PAC refines the discovery result in an agglomerative way. Experiments on object recognition and near-duplicate image retrieval validate the effectiveness and efficiency of our method.

Abstract

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