15536734
Annotating retrieval database with active learning
In this paper, we describe a retrieval system that uses hidden annotation to improve the performance. The contribution of this paper is a novel active learning framework that can improve the annotation efficiency. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. This list of probabilities serves as the basis of our active learning algorithm, as well as semantic features to determine the similarity between objects in the database. We show active learning has better performance than random sampling in all our experiments.
15005856
Security analysis for key generation systems using face images
In this paper, we analyze the security problem of user information associated key generation (UIAKG) systems. We consider three kinds of attacks from the hacker: the exhaustive search attack, the authentic key statistics attack and the device key statistics attack. Under each attack, we give the estimate of the number of guesses the hacker has to make in order to access the system. Such analysis provides useful guidelines in designing a UIAKG system. The analysis also suggests that a user-dependent UIAKG is more secure than a user-independent one. Two UIAKG systems using face images as inputs are designed and compared to support the above theoretical analysis.
12091703
Indexing and retrieval of 3D models aided by active learning
We demonstrate a system for indexing and retrieval of 3D models aided by active learning. We propose a new set of region-based features for 3D models. Each model is treated as a solid volume with a uniform density. Features such as the volume-surface ratio, the moment invariants and the Fourier transform coefficients are efficiently calculated from the mesh model directly. Comparable retrieval performance is achieved with other features such as the cord histogram, the 3D shape spectrum, etc. To further improve the performance, we incorporate hidden annotation into our system. We propose to use active learning to improve the annotation efficiency. We show that with active learning, the system can perform better than random annotation, and the retrieval result improves rapidly with the number of annotated samples. Moreover, relevance feedback is included in the system and combined with active learning, which provides better user-adoptive retrieval results.
15778343
Smart rebinning for compression of concentric mosaics
Concentric mosaics offer a quick solution to construct a virtual copy of a real environment, and navigate in the virtual environment. However, the huge amount of data associated with concentric mosaics is a heavy burden for its application. A 3D wavelet transform-based compressor has been proposed in previous work to compress the concentric mosaics. In this paper, we greatly improve the performance of the 3D wavelet coder with a data rearrangement mechanism called “smart rebinning”. The proposed scheme first aligns the concentric mosaic image shots along the horizontal direction and then rebins the shots into multi-perspective panoramas. Smart rebinning greatly improves the cross shot correlation and enables the coder to better explore the redundancy among shots. Experimental results show that the performance of the 3D wavelet coder improves an average of 4.3dB with the use of smart rebinning. The proposed coder outperforms MPEG-2 coding of concentric mosaics by an average of 3.7dB.
10867502
Robust Visual Tracking via Pixel Classification and Integration
We propose a novel framework for tracking non-rigid objects via pixel classification and integration (PCI). Given a new input frame, the tracker first performs object classification on each pixel and then finds the region that has the highest integral of scores. There are several key advantages of the proposed approach: it is computationally very efficient; it finds a global, instead of local (e.g., mean-shift), optimal solution within a search range; and it is inherently robust to different object scales with minimum extra computation. Within this framework, a mixture of long-term and short-term appearance model is further introduced to perform PCI. As a result, the tracker is able to adapt to both slow and rapid appearance changes without drifting. Challenging video sequences are presented to illustrate how the proposed tracker handles large motion, dramatic shape changes, scale variations, illumination variations and partial occlusions
11474786
A Self-Reconfigurable Camera Array
This paper presents a self-reconfigurable camera array system that captures video sequences from an array of mobile cameras, renders novel views on the fly and reconfigures the camera positions to achieve better rendering quality. The system is composed of 48 cameras mounted on mobile platforms. The contribution of this paper is twofold. First, we propose an efficient algorithm that is capable of rendering high-quality novel views from the captured images. The algorithm reconstructs a view-dependent multi-resolution 2D mesh model of the scene geometry on the fly and uses it for rendering. The algorithm combines region of interest (ROI) identification, JPEG image decompression, lens distortion correction, scene geometry reconstruction and novel view synthesis seamlessly on a single Intel Xeon 2.4 GHz processor, which is capable of generating novel views at 4-10 frames per second (fps). Second, we present a view-dependent adaptive capturing scheme that moves the cameras in order to show even better rendering results. Such camera reconfiguration naturally leads to a nonuniform arrangement of the cameras on the camera plane, which is both view-dependent and scene-dependent.
5598037
Supporting Information Reuse for Video Data Indexing by Using Color Information
In this paper, an object tracking framework assisted by image segmentation and color indexing techniques to support information reuse for video data is proposed. In order to obtain more accurate results of object tracking, a color indexing method called Color Label Criteria (CLC) is adopted to relax some of the restrictions and assumptions in object tracking in our previous work with low additional cost. Our experiment results on a real video sequence show that the CLC color indexing method is very helpful to achieve more robust object tracking.
17966080
User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this paper, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested under a couple of conditions.
9768130
Maximum Likelihood Study for Sound Pattern Separation and Recognition
The increasing needs of content-based automatic indexing for large musical repositories have led to extensive investigation in musical sound pattern recognition. Numerous acoustical sound features have been developed to describe the characteristics of a sound piece. Many of these features have been successfully applied to monophonic sound timbre recognition. However, most of those features failed to describe enough characteristics of polyphonic sounds for the purpose of classification, where sound patterns from different sources are overlapping with each other. Thus, sound separation technique is needed to process polyphonic sounds into monophonic sounds before feature extraction. In this paper, we proposed a novel sound source separation and estimation system to isolate sound sources by maximum likelihood fundamental frequency estimation and pattern matching of a harmonic sequence in our feature database.
45564986
Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this book chapter, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role the user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested to show the feasibility and effectiveness of the proposed multimedia data mining framework.
31582417
An Agent-Based Soft Computing Society
Soft computing (SC) techniques such as fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are complementary. Each SC technique has particular computational properties that make them suited for particular problems and not for others. Thus, in solving complex, real-world problems, we need to incorporate some SC techniques into the application systems to increase the systems' "intelligence". In this paper, we first propose an agent-based framework for integrating SC techniques into practical application systems. We then discuss the design and implementation of a platform independent soft computing support environment based on the framework. We call such an environment agent-based soft computing society. Such a society can facilitate the design of truly robust, flexible and adaptive hybrid intelligent systems.