Abstract

Instance retrieval (IR) is the problem of retrieving specific instances of a particular object, like a monument, from a collection of images. Currently, the most popular methods for IR use Bag of words (BoW) features for retrieval. However, a prominent problem for IR remains the tendency of BoW based methods to retrieve near-identical images as most relevant results. In this paper, we define diversity in IR as variation of physical properties among most relevant retrieved results for a query image. To achieve this, we propose both an ITML algorithm that re-fashions the BoW feature space into one that appreciates diversity better, and a measure to evaluate diversity in retrieval results for IR applications. Additionally, we also generate 200 hand-labeled images from the Paris dataset, for use in further research in this area. Experiments on the popular Paris dataset show that our method outperforms the standard BoW model …

Abstract

Egocentric cameras are wearable cameras mounted on a person's head or shoulder. With their ability to have first person view, such cameras are spawning new set of exciting applications in computer vision. Recognising activity of the wearer from an egocentric video is an important but challenging problem. The task is made especially difficult because of unavailability of wearer's pose as well as extreme camera shake due to motion of wearer's head. Solutions suggested so far for the problem, have either focussed on short term actions such as pour, stir etc. or long term activities such as walking, driving etc. The features used in both the styles are very different and the technique developed for one style often fail miserably on other kind. In this paper we propose a technique to identify if a long term or a short term action is present in an egocentric video segment. This allows us to have a generic first-person action …

Abstract

Automatic image annotation is the computer vision task of assigning a set of appropriate textual tags to a novel image. The aim is to eventually bridge the semantic gap of visual and textual representations with the help of these tags. This also has applications in designing scalable image retrieval systems and providing multilingual interfaces. Though a wide varieties of powerful machine learning algorithms have been explored for the image annotation problem in the recent past, nearest neighbor techniques still yield superior results to them. A challenge ahead of the present day annotation schemes is the lack of sufficient training data. In this paper, an active Learning based image annotation model is proposed. We leverage the image-to-image and image-to-tag similarities to decide the best set of tags describing the semantics of an image. The advantages of the proposed model includes: (a). It is able to output the …

Abstract

We present a two-stage, geometry-aware approach for matching SIFT-like features in a fast and reliable manner. Our approach first uses a small sample of features to estimate the epipolar geometry between the images and leverages it for guided matching of the remaining features. This simple and generalized two-stage matching approach produces denser feature correspondences while allowing us to formulate an accelerated search strategy to gain significant speedup over the traditional matching. The traditional matching punitively rejects many true feature matches due to a global ratio test. The adverse effect of this is particularly visible when matching image pairs with repetitive structures. The geometry-aware approach prevents such preemptive rejection using a selective ratio-test and works effectively even on scenes with repetitive structures. We also show that the proposed algorithm is easy to parallelize and implement it on the GPU. We experimentally validate our algorithm on publicly available datasets and compare the results with state-of-the-art methods.

Abstract

In this paper, we present an all-core implementation of Burrows Wheeler Compression algorithm that exploits all computing resources on a system. Our focus is to provide significant benefit to everyday users on common end-to-end applications by exploiting the parallelism of multiple CPU cores and additional accelerators, viz. many-core GPU, on their machines. The all-core framework is suitable for problems that process large files or buffers in blocks. We consider a system to be made up of compute stations and use a work-queue to dynamically divide the tasks among them. Each compute station uses an implementation that optimally exploits its architecture. We develop a fast GPU BWC algorithm by extending the state-of-the-art GPU string sort to efficiently perform BWT step of BWC. Our hybrid BWC with GPU acceleration achieves a 2.9× speedup over best CPU implementation. Our all-core framework allows concurrent processing of blocks by both GPU and all available CPU cores. We achieve a 3.06× speedup by using all CPU cores and a 4.87× speedup when we additionally use an accelerator i.e. GPU. Our approach will scale to the number and different types of computing resources or accelerators found on a system.

Abstract

Bidirectional path tracing (BDPT) can render highly realistic scenes with complicated lighting scenarios. The Light Vertex Cache (LVC) based BDPT method by Davidovic et al. [Davidovic et al. 2014 ˇ ] provided good performance on scenes with simple materials in a progressive rendering scenario. In this paper, we propose a new bidirectional path tracing formulation based on the LVC approach that handles scenes with complex, layered materials efficiently on the GPU. We achieve coherent material evaluation while conserving GPU memory requirements using sorting. We propose a modified method for selecting light vertices using the contribution importance which improves the image quality for a given amount of work. Progressive rendering can empower artists in the production pipeline to iterate and preview their work quickly. We hope the work presented here will enable the use of GPUs in the production pipeline with complex materials and complicated lighting scenarios.

Abstract

Freehand sketches often contain sparse visual detail. In spite of the sparsity, they are easily and consistently recognized by humans across cultures, languages and age groups. Therefore, analyzing such sparse sketches can aid our understanding of the neuro-cognitive processes involved in visual representation and recognition. In the recent past, Convolutional Neural Networks (CNNs) have emerged as a powerful framework for feature representation and recognition for a variety of image domains. However, the domain of sketch images has not been explored. This paper introduces a freehand sketch recognition framework based on" deep" features extracted from CNNs. We use two popular CNNs for our experiments--Imagenet CNN and a modified version of LeNet CNN. We evaluate our recognition framework on a publicly available benchmark database containing thousands of freehand sketches depicting everyday objects. Our results are an improvement over the existing state-of-the-art accuracies by 3%-11%. The effectiveness and relative compactness of our deep features also make them an ideal candidate for related problems such as sketch-based image retrieval. In addition, we provide a preliminary glimpse of how such features can help identify crucial attributes (eg object-parts) of the sketched objects.

Abstract

Freehand line sketches are an interesting and unique form of visual representation. Typically, such sketches are studied and utilized as an end product of the sketching process. However, we have found it instructive to study the sketches as sequentially accumulated composition of drawing strokes added over time. Studying sketches in this manner has enabled us to create novel sparse yet discriminative sketch-based representations for object categories which we term category-epitomes. Our procedure for obtaining these epitomes concurrently provides a natural measure for quantifying the sparseness underlying the original sketch, which we term epitome-score. We construct and analyze category-epitomes and epitome-scores for freehand sketches belonging to various object categories. Our analysis provides a novel viewpoint for studying the semantic nature of object categories.

Abstract

Preserving the cultural heritage of the performing arts raises difficult and sensitive issues, as each performance is unique by nature and the juxtaposition between the performers and the audience cannot be easily recorded. In this paper, we report on an experimental research project to preserve another aspect of the performing arts—the history of their rehearsals. We have specifically designed non-intrusive video recording and on-site documentation techniques to make this process transparent to the creative crew, and have developed a complete workflow to publish the recorded video data and their corresponding meta-data online as Open Data using state-of-the-art audio and video processing to maximize non-linear navigation and hypervideo linking. The resulting open archive is made publicly available to researchers and amateurs alike and offers a unique account of the inner workings of the worlds of theater and opera.

Abstract

Commoditizing idle computing resources by sharing them in a marketplace has gained increased attention in recent years as a potential disruption to the modern cloud-based service delivery. Recent initiatives have focused on scavenging for idle resources and provide suitable incentives accordingly. A recent work on resources marketplace has proposed a Marketplace for Compute Infrastructure that not only allows resource owners to get incentives by sharing resources to the marketplace but also ensures Service Level Agreements (SLAs), such as performance guarantees, for the computing jobs to be run by the shared resources.