1297870
An active learning framework for content-based information retrieval
We propose a general active learning framework for content-based information retrieval. We use this framework to guide hidden annotations in order to improve the retrieval performance. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. During training, the learning algorithm samples objects in the database and presents them to the annotator to assign attributes. For each sampled object, each probability is set to be one or zero depending on whether or not the corresponding attribute is assigned by the annotator. For objects that have not been annotated, the learning algorithm estimates their probabilities with biased kernel regression. Knowledge gain is then defined to determine, among the objects that have not been annotated, which one the system is the most uncertain. The system then presents it as the next sample to the annotator to which it is assigned attributes. During retrieval, the list of probabilities works as a feature vector for us to calculate the semantic distance between two objects, or between the user query and an object in the database. The overall distance between two objects is determined by a weighted sum of the semantic distance and the low-level feature distance. The algorithm is tested on both synthetic databases and real databases of 3D models. In both cases, the retrieval performance of the system improves rapidly with the number of annotated samples. Furthermore, we show that active learning outperforms learning based on random sampling.
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.
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.
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.
9847925
A multimedia semantic model for RTSP-based multimedia presentation systems
An abstract multimedia semantic model called the multimedia augmented transition network (MATN) model is proposed to model the RTSP-based (real-time streaming protocol) multimedia presentation systems. RTSP provides a set of methods to support the functionality of "VCR-like" remote control on the client side so that users are allowed to control playback of the streaming media. The MATN model is used to model RTSP actions as well as temporal relations and synchronization control of the media streams. The advantages of using the MATN model are its simplicity, effectiveness, and ease of modification. Furthermore, a formal definition of the MATN model and modeling of RTSP actions using the MATN model are presented in detail.
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.
207326756
Estimating the amount of defocus through a wavelet transform approach
This paper presents a new technique for defocus estimation of a captured image. In our method, a ratio of the wavelet coefficients of high frequency correspond to a same image point at two different levels is used. For an edge point, it is shown that the ratio is related to the amount of defocus. Let α be the ratio of wavelet coefficients at the first level to that at the second level. The value of α decreases as the amount of defocus increase. In our experiments of iris image analysis, when α is larger than 0.5 the number of feature points in an image almost remain constant. It means that the image is little defocused and available for image recognition. Compared with Fourier methods, this technique is more robust. In addition, this method is fast enough to be used in auto-focus system for tracking moving objects.
83568
Fingerprint Reference Point Detection Based on Local Axial Symmetry
Reference point detection is an important process for fingerprint analysis. In this paper, we propose a novel feature which is named local axial symmetry (LAS) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. Experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods
2688646
A unified framework for image database clustering and content-based retrieval
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called <i>Markov Model Mediators</i> (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.