Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back‐dropout transfer learning (NB‐TL), which utilizes images that have been misclassified and further performs back‐dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB‐TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We present an in-depth study of two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate detection could achieve state-of-the-art performance on the UCSD dataset [14].

Abnormal event detection has been a challenge due to the lack of complete normal information in the training data and the volatility of the definitions of both normality and abnormality. Recent research applying sparse representation has shown its effectiveness in the expression of normal patterns. Despite progress in this area, the relationship of atoms within the dictionary is commonly neglected, thereafter anomalies which are detected based on reconstruction error could brings high false alarm - noise or infrequent normal visual features could be wrongly detected as anomalies, especially when the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms compared to state-of-the-art dictionaries especially when the training set is small, which is demonstrated on Anomaly Stairs dataset.

We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality measurement based on the difference between the normal space and local space. Specifically, we provide a reasonable normal bases through repeated K spectral clustering. Then for each testing feature we first use temporal neighbors to form a local space. An abnormal event is found if any abnormal feature is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness.

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-of-words” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions. Our approach yields a competitive result on the KTH dataset compare to state-of-the-art methods. On the more challenging UCF Sports dataset, we obtain 95.21%, which is approximately 4% better than the current best published results.

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 re-trieval validate the effectiveness and efficiency of our method.

This paper proposes a novel content-based copy retrieval scheme for video copy identification. Its goal is to detect matches between a doubtful video and the ones stored in the database of the legal holders of the videos. Due to various transformations the copy may has, we use visual words vector as a representation of a frame which is based on SIFT descriptor. Unlike traditional bag-of-words (BoW) based approach applied in semantic retrieval, in which the temporal variation during the video is always neglected, our matching algorithm takes into account spatial and temporal distances between a query clip and the one in database. Experiments show robustness and effectiveness of our approach according to various single and compound transformations.