15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
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.
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
2447873
Multilabel SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
9891360
Image retrieval with SVM active learning embedding Euclidean search
Image retrieval with relevance feedback suffers from the small sample problem. Recently, SVM active learning has been proposed to tackle this problem, showing promising results. However, a small but sufficient number of initially labelled samples are still required to ensure subsequent efficient active learning and good retrieval performance. In the existing method, the user is asked to label more images before active learning starts. In this paper, a method of embedding Euclidean search into SVM active learning is proposed. With the help of Euclidean search, the adverse effect on retrieval performance due to lack of initially labelled samples can be reduced. Experimental results demonstrate the improvement by the proposed method, especially when the number of initially labelled samples is small.
16543010
A novel multi-resolution video representation scheme based on kernel PCA
Content-based video analysis calls for efficient and flexible video representation. In this paper, a novel multi-resolution video representation (MRVR) scheme is proposed and realized by performing the kernel principal component analysis (KPCA) on the low-level visual features extracted from a video sequence. By simply changing the kernel parameters or the dimensionality of the subspace, this scheme can represent video content from coarser to finer levels in the subspace, according to its intrinsic structure. An application of keyframe extraction is investigated to show the advantages of this representation scheme. Furthermore, based on this scheme, a two-level video summarization approach is proposed to represent long video sequences. The experimental results of both short and long video sequences have demonstrated the effectiveness and flexibility of the proposed video representation scheme.
1037558
PhoenixG: a unified management framework for industrial information grid
The industrial information grid is a special kind of system, the users of which exclusively own geographically distributed computing resources for business service, and try to maintain the lowest total cost of ownership while guaranteeing quality of service. In this paper, we classify the industrial information grid as an extension to grid problem; develop a unified management framework for new management paradigm, which supports the distribution of administration labor and collaboration of system administrator at different locations; propose a self-organizing algorithm, which supports the initial establishment, daily management and exception processing of industrial information grid. Finally, we evaluate the performance of system management, and analyze the management overhead with this new management paradigm.
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.
18869116
Thread-Associative Memory for Multicore and Multithreaded Computing
Presented in this paper is the thread-associative memory microarchitecture for multicore and multithreaded processor design. Memory contention among concurrent threads in chip multithreaded processing has become a limiting factor for performance improvement. The proposed thread-associative memory addresses this challenge by incorporating thread-specific information explicitly into on-chip memory hardware. The proposed technique can be utilized at different levels of memory hierarchy. Furthermore, it is not just a technique for performance enhancement but also a solution for energy efficiency. Trace-driven simulations on a 32KB L1 data cache demonstrate 36.6% maximum performance improvement and up to 15.1% total energy reduction, with 20.3% dynamic energy reduction and 9.9% leakage energy reduction
8141740
A Vector-Based Approach to Broadcast Audio Database Indexing and Retrieval
This paper proposes a novel framework to index and retrieve audio content from broadcast database that contains both speech and music. In this framework, we model the acoustic events using hidden Markov models, which are then used to decode the audio content. The decoding results in the form of acoustic token sequence and acoustic lattice are used to generate features for indexing and retrieval with the vector space model. Experiments were carried out on the TRECVID database and the results showed that the proposed framework is effective in audio information retrieval. The results also showed that the features generated from the acoustic lattice provide more accurate information than token sequence.
15406626
A Lightweight Sensor Network Management System Design
In this paper, we propose a lightweight and transparent management framework for TinyOS sensor networks, called L-SNMS, which minimizes the overhead of management functions, including memory usage overhead, network traffic overhead, and integration overhead. We accomplish this by making L-SNMS virtually transparent to other applications hence requiring minimal integration. The proposed L-SNMS framework has been successfully tested on various sensor node platforms, including TelosB, MICAz and IMote2.