Abstract

A major requirement in the design of robust OCRs is the invariance of feature extraction scheme with the popular fonts used in the print. Many statistical and structural features have been tried for character classification in the past. In this paper, we get motivated by the recent successes in object category recognition literature and use a spatial extension of the histogram of oriented gradients (HOG) for character classification. Our experiments are conducted on 1453950 Telugu character samples in 359 classes and 15 fonts. On this data set, we obtain an accuracy of 96-98% with an SVM classifier.

Abstract

Video Scene Segmentation is an important problem in computer vision as it helps in efficient storage, indexing and retrieval of videos. Significant amount of work has been done in this area in the form of shot segmentation techniques and they often give reasonably good results. However, shots are not of much importance for the semantic analysis of the videos. For semantic and meaningful analysis of the videos (e.g. movies), scene is more important since it captures one complete unit of action. People have tried different approaches in scene segmentation but almost all of them use color, texture etc. to compute scene boundaries. In this paper, we propose a new algorithm based on a Bag of Words(BoW) representation which computes semantic similarity between shots using a Bipartite Graph Model (BGM). Based on semantic similarity, we detect the scene boundaries in the movie. We have tested our algorithm on a multiple Hollywood movies, and the proposed method is found to give good results.

Abstract

The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments.

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

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

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

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

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

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

In this paper, we present a practical vision based Simultaneous Localization and Mapping (SLAM) system for a highly dynamic environment. We adopt a multibody Structure from Motion (SfM) approach, which is the generalization of classical SfM to dynamic scenes with multiple rigidly moving objects. The proposed framework of multibody visual SLAM allows choosing between full 3D reconstruction or simply tracking of the moving objects, which adds flexibility to the system, for scenes containing non-rigid objects or objects having insufficient features for reconstruction. The solution demands a motion segmentation framework that can segment feature points belonging to different motions and maintain the segmentation with time. We propose a realtime incremental motion segmentation algorithm for this purpose. The motion segmentation is robust and is capable of segmenting 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 motion segmentation module is tightly coupled with feature tracking and visual SLAM, by exploring various feed-backs in between these modules. The integrated system can simultaneously perform realtime visual SLAM and tracking of multiple moving objects using only a single monocular camera.