17047961
Image annotations by combining multiple evidence & wordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
15085728
Object Detection for Hierarchical Image Classification
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval in the image domain is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring that relevant information is not overlooked (high recall). The unstructured format of images tends to resist the deployment of standard search mechanism and classification techniques. As a method to provide better organization of images, clustering is important aspect for effective image retrieval. For this, we need to identify objects that appear in images. In this paper, we propose using an automatic scalable object boundary detection algorithm based on edge detection and region growing techniques to accurately identify all object boundaries that appear in images, and an efficient merging algorithm for joining adjacent regions which uses an adjacency graph to avoid the over-segmentation of regions. To illustrate the effectiveness of our algorithm in automatic image classification we implement a very basic system and observe how well our approach works when objects in images have varying degrees of complex organization along with shading and highlights.
46376386
Improving Image Annotations Using WordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
3194016
A Novel Model of Artificial Immune Network and Simulations on Its Dynamics
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. In this model, a B cell makes a key role that takes antigens in, so as to generate antibodies as its outputs. Under five different kinds of adjustment by suppressor T cells, number of antibodies will keep to a certain degree through influencing the B cell’s activation. On the other hand, with help T cells, different B cells could cooperate from each other, which makes the system’s dynamic behavior appear more complex, such as phenomena of limit cycle, chaos, etc. Simulative results show that limit cycle and chaos may exist simultaneously when four units are in connection, and the network’s characteristic has a close relationship with the intensity of suppressor T cell’s function.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
38163613
Bayesian Learning for Image Retrieval Using Multiple Features
Image retrieval using multiple features often uses explicit weights that represent the importance of the features in their similarity metrics. In this paper, a novel retrieval method based on Bayesian Learning is presented. Instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception. Thus, the process of feature combination is adaptive and approximate to a user's perception. Experimental results demonstrate the significance of this method for improving the retrieval efficiency.
16020986
Towards achieving reliable and high-performance nanocomputing via dynamic redundancy allocation
Nanoelectronic devices are considered to be the computational fabrics for the emerging nanocomputing systems due to their ultra-high speed and integration density. However, the imperfect bottom-up self-assembly fabrication leads to excessive defects that have become a barrier for achieving reliable computing. In addition, transient errors continue to be a problem. The massive parallelism rendered by nanoscale integration opens up new opportunities but also poses challenges on how to manage such massive resources for reliable and high-performance computing. In this paper, we propose a nanoarchitecture solution to address these emerging challenges. By using dynamic redundancy allocation, the massive parallelism is exploited to jointly achieve fault (defect/error) tolerance and high performance. Simulation results demonstrate the effectiveness of the proposed technique under a range of fault rates and operating conditions.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.