Abstract

Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. Content free image retrieval (CFIR) methods, and their complementary characteristics to CBIR has not received enough attention in the literature. Performance of CBIR is constrained by the semantic gap between the feature representations and user expectations, while CFIR suffers with sparse logs and cold starts. We fuse both of them in a Bayesian framework to design a hybrid image retrieval system by overcoming their shortcomings. We validate our ideas and report experimental results, both qualitatively and quantitatively. We use our indexing scheme to efficiently represent both features and logs, thereby enabling scalability to millions of images.

Abstract

Biometric authentication over public networks leads to a variety of privacy issues that needs to be addressed before it can become popular. The primary concerns are that the biometrics might reveal more information than the identity itself, as well as provide the ability to track users over an extended period of time. In this paper, we propose an authentication protocol that alleviates these concerns. The protocol takes care of user privacy, template protection and trust issues in biometric authentication systems. The protocol uses asymmetric encryption, and captures the advantages of biometric authentication. The protocol provides non-repudiable identity verification, while not revealing any additional information about the user to the server or vice versa. We show that the protocol is secure under various attacks. Experimental results indicate that the overall method is efficient to be used in practical scenarios.

Abstract

We propose an approach to restore severely degraded document images using a probabilistic context model. Unlike traditional approaches that use previously learned prior models to restore an image, we are able to learn the text model from the degraded document itself, making the approach independent of script, font, style, etc. We model the contextual relationship using an MRF. The ability to work with larger patch sizes allows us to deal with severe degradations including cuts, blobs, merges and vandalized documents. Our approach can also integrate document restoration and super-resolution into a single framework, thus directly generating high quality images from degraded documents. Experimental results show significant improvement in image quality on document images collected from various sources including magazines and books, and comprehensively demonstrate the robustness and adaptability of the approach. It works well with document collections such as books, even with severe degradations, and hence is ideally suited for repositories such as digital libraries

Abstract

Search and retrieval is gaining importance in the ink domain due to the increase in the availability of online handwritten data. However, the problem is challenging due to variations in handwriting between various writers, digitizers and writing conditions. In this paper, we propose a retrieval mechanism for online handwriting, which can handle different writing styles, specifically for Indian languages. The proposed approach provides a keyboard-based search interface that enables to search handwritten data from any platform, in addition to pen-based and example-based queries. One of the major advantages of this framework is that information retrieval techniques such as ranking relevance, detecting stopwords and controlling word forms are extended to work with search and retrieval in the ink domain. The framework also allows cross-lingual document retrieval across Indian languages

Abstract

Widespread use of surveillance cameras in offices and other business establishments, pose a significant threat to the privacy of the employees and visitors. The challenge of introducing privacy and security in such a practical surveillance system has been stifled by the enormous computational and communication overhead required by the solutions. In this paper, we propose an efficient framework to carry out privacy preserving surveillance. We split each frame into a set of random images. Each image by itself does not convey any meaningful information about the original frame, while collectively, they retain all the information. Our solution is derived from a secret sharing scheme based on the Chinese Remainder Theorem, suitably adapted to image data. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. The system installed in an office like environment can effectively detect and track people, or solve similar surveillance tasks. Our proposed paradigm is highly efficient compared to Secure Multiparty Computation, making privacy preserving surveillance, practical.

Abstract

Graphs can be used for effective representation of images for recognition and retrieval purposes. The problem is often to find a proper structure that can efficiently describe an image and can be matched in reasonably low computational expense. The standard solutions to the graph matching problem are computationally expensive since the search space involves all permutations of the nodesets. We compare two graphical representations called the Nearest-Neighbor Graphs and the Collocation Trees, for the goodness of fit and the computational expense involved in matching. Various schemes to index the graphical structures have also been discussed.

Abstract

The Oxford/IIIT team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, both based on a combination of visual features. One used a SVM classifier using a linear combination of kernels, the other used a random forest classifier. For both methods, we trained all high-level features using publicly available annotations [3]. The advantage of the random forest classifier is the speed of training and testing. In addition, for the people feature, we took a more targeted approach. We used a real-time face detector and an upper body detector, in both cases running on every frame. Our best performing submission, C OXVGG 1 1, which used a rank fusion of our random forest and SVM approach, achieved an mAP of 0.101 and was above the median for all but one feature. In the interactive search task, our team came third overall with an mAP of 0.158. The system used was identical to last year with the only change being a source of accurate upper body detections.

Abstract

Many events are captured using multiple cameras today. Frames of each video stream have to be synchronized and aligned to a common time axis before processing them. Synchronization of the video streams necessarily needs a hardware based solution that is applied while capturing. The alignment problem between the frames of multiple videos can be posed as a search using traditional measures for image similarity. Multiview relations and constraints developed in Computer Vision recently can provide more elegant solutions to this problem. In this paper, we provide two solutions for the video frame alignment problem using two view and three view constraints. We present solutions to this problem for the case when the videos are taken using affine cameras and for general projective cameras. Excellent experimental results are achieved by our algorithms.

Abstract

This work aims to enhance the matching and retrieval performance over image datasets which have similar spatial structures that occur very frequently. Instead of treating images as bags of features, we try to encode the spatial relationships in the representation. This process would help to resolve the ambiguity when two classes of images have similar sets of features although in different spatial arrangements. To demonstrate the fact a sizeable dataset of license plate images is used. We have proposed a method to use graphs to encode the spatial relationships among features. The problem of image matching thus turns to finding the maximum similarity between labelled graphs. It is shown that the precision of the retrieved results increases with this matching scheme since most of the false matches are eliminated.

Abstract

Biometrics traits such as fingerprints, hand geometry, face and voice verification provide a reliable alternative for identity verification and are gaining commercial and high user acceptibilty rate. Hand geometry based biometric verification has proven to be the most suitable and acceptable biometric trait for medium and low security application. Geometric measurements of the human hand have been used for identity authentication in a number of commercial systems. However not much research has been done in the area of selection of the optimal discriminating features for hand-gemetry based authentication system. In this paper, We argue that the biometric verification problem can be best posed as the single-class problem. We propose to apply Biased Discriminant Analysis and Nonparametric Discriminant Analysis in order to transform the features into a new space where the samples are well separated.