17340449
On scalability of active learning for formulating query concepts
Query-by-example and query-by-keyword both suffer from the problem of "aliasing," meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in dataset size and in concept complexity. We present remedies, explain limitations, and discuss future directions that research might take.
18150673
The anatomy of a multimodal information filter
The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.
10743717
Support vector machine active learning for image retrieval
Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
2439726
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary instances as training data to substantially reduce n. Our proposed algorithm is motivated by the result of (Burges, 1999) that, removing non-support vectors from the training set does not change SVM training results. Our algorithm eliminates instances that are likely to be non-support vectors. In the concept-independent preprocessing step of our algorithm, we prepare nearest-neighbor lists for training instances. In the concept-specific sampling step, we can then effectively select useful training data for each target concept. Empirical studies show our algorithm to be effective in reducing n, outperforming other competing downsampling algorithms without significantly compromising testing accuracy.
9408458
PBIR-MM: multimodal image retrieval and annotation
We demonstrate PBIR-MM, an integrated system that we have built for conducting multimodal image retrieval. The system combines the strengths of content-based soft annotation (CBSA), multimodal relevance feedback through active learning, and perceptual distance formulation and indexing. PBIR-MM supports multimodal query and annotation in any combination of its three basic modes: seed-by-nothing, seed-by-keywords, and seed-by-content. We demonstrate PBIR-MM on a couple of very large image sets provided by image vendors and crawled from the Internet.
7236137
Efficient top-k hyperplane query processing for multimedia information retrieval
A query can be answered by a binary classifier, which separates the instances that are relevant to the query from the ones that are not. When kernel methods are employed to train such a classifier, the class boundary is represented as a hyperplane in a projected space. Data instances that are farthest from the hyperplane are deemed to be most relevant to the query, and that are nearest to the hyperplane to be most uncertain to the query. In this paper, we address the twin problems of efficient retrieval of the approximate set of instances (a) farthest from and (b) nearest to a query hyperplane. Retrieval of instances for this hyperplane-based query scenario is mapped to the range-query problem allowing for the reuse of existing index structures. Empirical evaluation on large image datasets confirms the effectiveness of our approach.
12953441
A Simultaneous Search Problem
Abstract. We introduce a new search problem motivated by computational metrology. The problem is as follows: we would like to locate two unknown numbers x,y ∈ [0,1] with as little uncertainty as possible, using some given number k of probes. Each probe is specified by a real number r∈ [0,1] . After a probe at r , we are told whether x≤ r or x \geq r , and whether y≤ r or y\geq r . We derive the optimal strategy and prove that the asymptotic behavior of the total uncertainty after k probes is 13/7 2-(k+1)/2 for odd k and 13/10 2-k/2 for even k .
11249256
A unified framework for resolving ambiguity in copy detection
Copy detection is an important component of digital rights management and can be implemented using a retrieval-based approach. Under this approach, a query image, suspected to be a copy, is compared against all the images in the owner database. The comparison is done based on a distance metric in feature space. The performance of such a system depends on the mutual separation of the feature representation of the images in the database. In this paper we propose a framework that increases this mutual separation by literally shifting them away from each other. The idea of modifying the features derives its inspiration from the field of watermarking. It is also important to make sure that the semantics of the images do not change after modification. Thus the focus of this paper is on how to modify the images in the database, so that the mutual separation between the images in feature space is above a certain threshold and the distortion induced is minimized. This problem can be formulated as a non-convex optimization problem which is difficult to solve. We propose a restriction of the problem and solve it using second-order cone programming. We present a practical implementation of our framework, named RAM, which uses AFMT as the feature representation. We conduct experiments to test the performance of RAM.
12117585
Distributed multivariate regression based on influential observations
Large-scale data sets are sometimes logically and physically distributed in separate databases. The issues of mining these data sets are not just their sizes, but also the distributed nature. The complication is that communicating all the data to a central database would be too slow. To reduce communication costs, one could compress the data during transmission. Another method is random sampling. We propose an approach for distributed multivariate regression based on sampling and discuss its relationship with the compression method. The central idea is motivated by the observation that, although communication is limited, each individual site can still scan and process all the data it holds. Thus it is possible for the site to communicate only influential samples without seeing data in other sites. We exploit this observation and derive a method that provides tradeoff between communication cost and accuracy. Experimental results show that it is better than the compression method and random sampling.
12976760
XML descriptor based approach for real time data messaging
This paper presents an overview of the real time markup language (RTML). RTML is a XML profile which provides the syntactic representation for describing the semantics of real time data for exchange over distributed networked real time systems. For the basis of interoperability, this profile is described in the XML schema language. This paper describes the background of this work and shows how the vocabularies are developed, and how it derives the extensibility of XML schema in aiding the definition of data in real time systems in order to achieve the goal of interoperability
3079245
Statistics- and Spatiality-Based Feature Distance Measure for Error Resilient Image Authentication
Content-based image authentication typically assesses authenticity based on a distance measure between the image to be tested and its original. Commonly employed distance measures such as the Minkowski measures (including Hamming and Euclidean distances) may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties in features. This paper proposes a feature distance measure for content-based image authentication based on statistical and spatial properties of the feature differences. The proposed statistics- and spatiality-based measure (SSM) is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations result in global distortions. A statistical measure, kurtosis, is used to assess the shape of the feature difference distribution; a spatial measure, the maximum connected component size, is used to assess the degree of object concentration of the feature differences. The experimental results have confirmed that our proposed measure is better than previous measures in distinguishing malicious manipulations from acceptable ones.