Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.
In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.
重大公共活动,比如大型赛事,由于其参与人数众多,影响力广泛,一直是恐怖分子的重要攻击目标.因此,重大公共活动的安保问题也是各国政府必须面对的一项难题.由于公共活动通常场地复杂,参与者多样,而安全部门可支配的安保资源有限,如何最大限度地利用有限的资源保障活动安全进行成为了一项极具挑战的任务.本文以博弈模型来描述重大公共活动的安保问题,该模型既考虑了公共活动本身人流量与时间相关的特点,也考虑了安全部门与潜在的恐怖分子的复杂的策略空间.基于此模型,本文研究了安保资源转移时间可忽略与转移时间不可忽略两种情况,并分别提出算法SCOUT-A(Scheduling seCurity res Ources in pUblic evenTs with no relocating delAy)和SCOUT-C(Scheduling seCurity resOurces in pUblic evenTs against Continuous strategy space)来求解安保部门的最优策略.实验证明,本文提出的算法比已有的算法为安保部门带来更好的收益.
The cognitive model ABGP is a special model for agents,which consists of awareness,beliefs,goals and plans. The ABGP agents obtain the knowledge directly from the natural scenes only through some single preestablished rules like most agent architectures. Inspired by the biological visual cortex( V1) and the higher brain areas perceiving the visual feature,deep convolution neural networks( CNN) are introduced as a visual pathway into ABGP to build a novel visual awareness module. Then a rat-robot maze search simulation platform is constructed to validate that CNN can be used for the awareness module of ABGP. According to the simulation results,the rat-robot implemented by the ABGP with the CNN awareness module reaches the excellent performance of recognizing guideposts,which directly enhances the capability of the communication between the agent and the natural scenes and improves the ability to recognize the real world,which successfully demonstrates that an agent can independently plan its path in terms of the natural scenes.
