Abstract

In this paper, we present an approach to discover characteristic patterns in videos. We characterize the videos based on frequently occurring patterns like scenes, characters, sequence of frames in an unsupervised setting. With our approach, we are able to detect the representative scenes and characters of movies. We also present a method for detecting video stop-words in broadcast news videos based on the frequency of occurrence of sequence of frames. These are analogous to stop-words in text classification and search. We employ two different video mining schemes; both aimed at detecting frequent and representative patterns. For one of our mining approaches, we use an efficient frequent pattern mining algorithm over a quantized feature space. Our second approach uses a Random Forest to first represent video data as sequences, and then mine the frequent patterns. We validate the proposed approaches on broadcast news videos and our database of 81 Oscar winning movies.

Abstract

One of the most common computer vision tasks is that of recognizing the category of objects present in a given image. Previous work has mostly focused on building accurate classifiers based on carefully selected features. Classification is often carried on individual test images, while most of the practical situations, such as webscale image indexing, demand the simultaneous classification of a large collection of images. This is especially true for real-world datasets, that already contain numerous un-indexed images and videos. In this paper, we work towards developing a computationally efficient approach towards object recognition, that is inspired by retrieval schemes. We perform an offline indexing of the features from the collection, so that the classifier only needs to work on a small subset of the entire feature set. Over a set of 2 Million features extracted from 7000 images, classification against 5 object categories using a standard SVM would require more than 260 hours. Over the same test case, the classification time using our indexing based approach is reduced to less than 13 hours. The compromise on the accuracy is less than 7% for the 20X speedup achieved.

Abstract

Concerns on widespread use of biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as shown by biometric cryptosystems. In this paper, we propose a provably secure and blind biometric authentication protocol, which addresses the concerns of user’s privacy, template protection, and trust issues. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the biometric to the authenticating server or vice-versa. As the protocol is based on asymmetric encryption of the biometric data, it captures the advantages of biometric authentication as well as the security of public key cryptography. The authentication protocol can run over public networks and provide nonrepudiable identity verification. The encryption also provides template protection, the ability to revoke enrolled templates, and alleviates the concerns on privacy in widespread use of biometrics. The proposed approach makes no restrictive assumptions on the biometric data and is hence applicable to multiple biometrics. Such a protocol has significant advantages over existing biometric cryptosystems, which use a biometric to secure a secret key, which in turn is used for authentication. We analyze the security of the protocol under various attack scenarios. Experimental results on four biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.

Abstract

This paper introduces an efficient privacy-preserving protocol for distributed K-means clustering over an arbitrary partitioned data, shared among N parties. Clustering is one of the fundamental algorithms used in the field of data mining. Advances in data acquisition methodologies have resulted in collection and storage of vast quantities of user’s personal data. For mutual benefit, organizations tend to share their data for analytical purposes, thus raising privacy concerns for the users. Over the years, numerous attempts have been made to introduce privacy and security at the expense of massive additional communication costs. The approaches suggested in the literature make use of the cryptographic protocols such as Secure Multiparty Computation (SMC) and/or homomorphic encryption schemes like Paillier’s encryption. Methods using such schemes have proven communication overheads. And in practice are found to be slower by a factor of more than 106. In light of the practical limitations posed by privacy using the traditional approaches, we explore a paradigm shift to side-step the expensive protocols of SMC. In this work, we use the paradigm of secret sharing, which allows the data to be divided into multiple shares and processed separately at different servers. Using the paradigm of secret sharing, allows us to design a provably-secure, cloud computing based solution which has negligible communication overhead compared to SMC and is hence over a million times faster than similar SMC based protocols

Abstract

An adaptive partition based Random Forests classifier for outdoor terrain classification is presented in this paper. The classifier is a combination of two underlying classifiers. One of which is a random forest learnt over bootstrapped or offline dataset, the second is another random forest that adapts to changes on the fly. Posterior probabilities of both the static and changing/online classifiers are fused to assign the eventual label for the online image data. The online classifier learns at frequent intervals of time through a sparse and stable set of tracked patches, which makes it lightweight and real-time friendly. The learning which is actuated at frequent intervals during the sojourn significantly improves the performance of the classifier vis-a-vis a scheme that only uses the classifier learnt offline or at bootstrap. The method is well suited and finds immediate applications for outdoor autonomous driving where the classifier needs to be updated frequently based on what shows up recently on the terrain and without largely deviating from those learnt at bootstrapping. The role of the partition based classifier to enhance the performance of a regular multi class classifier such as random forests and multi class SVMs is also summarized in this paper.

Abstract

Detection of moving objects is a key component in mobile robotic perception and understanding of the environment. In this paper, we describe a realtime independent motion detection algorithm for this purpose. The method is robust and is capable of detecting difficult degenerate motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to the use of efficient geometric constraints and a probability framework which propagates the uncertainty in the system. The proposed independent motion detection framework integrates seamlessly with existing visual SLAM solutions. The system consists of multiple modules which are tightly coupled so that one module benefits from another. The integrated system can simultaneously detect multiple moving objects in realtime from a freely moving monocular camera.

Abstract

1. Oxford-IIIT combined: a spatial pyramid intersection kernel SVM image classifier, a sliding-window randomforest object detector, a sliding-window intersection kernel SVM object detector, and a discriminative constellation model facial feature extractor. For each of the twenty features, methods were ranked based on their performance on a validation set and associated to successive runs by decreasing performance. For training, TRECVID annotations were manually corrected and augmented with object bounding boxes, and additional training data was used for under-represented features such as Airplane flying. 2. The different methods yielded a significantly different performance depending on the feature, as expected by their design. 3. The image classifier worked better for scene-level features such as Cityscape, Classroom, Doorway, while the object detectors worked better for Boat or ship, Bus, Person riding a bicycle, and the face feature extractor worked well for Female face closeup. 4. Three conclusions can be drawn: (i) different features are addressed better by specialised methods, (ii) removal of noise from TRECVID annotations (iii) additional data for under-represented features significantly improve performance.

Abstract

In this paper, we empirically study the performance of a set of pattern classification schemes for character classification problems. We argue that with a rich feature space, this class of problems can be solved with reasonable success using a set of statistical feature extraction schemes. Experimental validation is done on a data set (of more than 5,00,000 characters) collected and annotated from books printed primarily in Malayalam. Scope of this study include (a) applicability of a spectrum of classifiers and features (b) scalability of classifiers (c) sensitivity of features to degradation (d) generalization across fonts and (e) applicability across scripts

Abstract

Contemporary approaches to semantic indexing for bag of words image retrieval do not adapt well when the image or video collections dynamically get modified. In this paper, We propose an on-line incremental semantic indexing scheme for image retrieval in dynamic image collections. Our main contributions are in the form of a method and a data structure that tackle representation of the term document matrix and on-line semantic indexing where the database changes. We introduce a bipartite graph model (BGM) which is a scalable data structure that aids in online semantic indexing. It can also be incrementally updated. BGM uses tf-idf values for building a semantic bipartite graph. We also introduce a cash flow algorithm that works on the BGM to retrieve semantically relevant images from the database. We examine the properties of both BGM and cash flow algorithm through a series of experiments. Finally, we demonstrate how they can be effectively implemented to build large scale image retrieval systems in an incremental manner.

Abstract

Many of the successful multimedia retrieval systems focus on developing efficient and effective video retrieval solutions with the help of appropriate index structures. In these systems, the query is an example video and the retrieved results are similar video clips which are available apriori in the database. In this paper, we address a complementary problem of filtering a video stream based on a set of given examples. By filtering, we mean to detect, accept or reject the part of a video stream matching any of the given examples. This requires matching of example videos with the on-line video stream. Since the concepts of interest could be complex, we avoid explicit learning of a representation from the example videos to characterize the visual event present in the examples. We model the problem as simultaneous on-line spotting of multiple examples in a video stream. We employ a vocabulary trie for the filtering purpose and demonstrate the applicability of the technique in a variety of situations.