Retrieving visual object from a large-scale video dataset is one of multimedia research focuses but a challenging task due to imprecise object extraction and partial occlusion. This paper presents a novel approach to efficiently encode and retrieve visual objects, which addresses some practical complications in surveillance videos. Specifically, we take advantage of the mask information to assist object representation, and develop an encoding method by utilizing highly nonlinear mapping with a deep neural network. Furthermore, we add some occluded noise into the learning process to enhance the robustness of dealing with background noise and partial occlusions. A real-life surveillance video data containing over 10 million objects are built to evaluate the proposed approach. Experimental results show our approach significantly outperforms state-of-the-art solutions for object retrieval in large-scale video dataset.

To handle high dimensional variables in real world, especially multimedia data, dimension reduction techniques provide effective solutions for feature selection which makes the problem easy to deal in a lower dimension subspace. However, the primary problem with traditional dimension reduction method is to estimate intrinsic dimension of manifold supporting the raw data. Since all existing approaches of dimension reduction need to set the target dimension first. And for task of classification, the dimension we need maybe different from the intrinsic one, which means feature with intrinsic dimension may offer too much information for the classification task. In this paper, we propose a method to estimate classification related dimension with the help of RBM (Restricted Boltzmann Machine) and SVM classifiers. RBM is used for dimension reduction by mapping raw data to a low dimensional hidden space, and SVM classifier is used to test information preserving ability of this hidden space. An optimal dimension is selected as the minimum one that preserves all the classification related information of novel vectors. Further, a novel initialization strategy is proposed to speed up the training of RBM. These new methods are with low time consumption and memory cost compared to many other approaches of dimension estimation. Experiments and comparisons on several synthetic and realistic datasets show the superiority of the proposed method.

The bag-of-visual-words (BOW) model has been widely used in the field of scene classification. Since it ignores the spatial information, the spatial-pyramid-matching (SPM) model [1] was presented by partitioning the image into increasingly fine blocks and computing histograms of local features in each block. However, the spatial symmetry has never been considered explicitly in scene classification as we known. In this paper, a novel descriptor named weakly spatial symmetry (WSS) is proposed to boost the performance of image classification. After region segmentation, the spatial symmetry is represented by L1 distances of region histograms. Four kinds of spatial symmetry are extracted in blocks of increasing scales as in SPM [1]. The WSS descriptor can be used independently or combined with BOW or SPM for scene classification. Experiments on scene-15 and caltech 101 dataset demonstrate the effectiveness of the proposed approach.