Abstract

In this paper we propose a framework for recognition and retrieval tasks in the context of scene text images. In contrast to many of the recent works, we focus on the case where an image-specific list of words, known as the small lexicon setting, is unavailable. We present a conditional random field model defined on potential character locations and the interactions between them. Observing that the interaction potentials computed in the large lexicon setting are less effective than in the case of a small lexicon, we propose an iterative method, which alternates between finding the most likely solution and refining the interaction potentials. We evaluate our method on public datasets and show that it improves over baseline and state-of-the-art approaches. For example, we obtain nearly 15% improvement in recognition accuracy and precision for our retrieval task over baseline methods on the IIIT-5K word dataset, with a large lexicon containing 0.5 million words.

Abstract

Computer vision applications today run on a wide range of mobile devices. Even though these devices are becoming more ubiquitous and general purpose, we continue to see a whole spectrum of processing and storage capabilities within this class. Moreover, even as the processing and storage capacity of devices are increasing, the complexity of vision solutions and the variety of use cases create greater demands on these resources. This requires appropriate adaptation of the mobile vision applications with minimal changes in the algorithm or implementation. In this work, we focus on optimizing the memory usage for storage intensive vision applications. In this paper, we propose a framework to configure memory requirements of vision applications. We start from a gold standard desktop application, and reduce the the size for a given the memory constraint. We formulate the storage optimization problem as mixed integer programming (mip) based optimization to select the most relevant subset of data to be retained. For large data sets, we use a greedy approximate solution which is empirically comparable to the optimal mip solution. We demonstrate the method in two different use cases: (a) Instance retrieval task where an image of a query object is looked up for instant recognition/annotation, and (b) Augmented reality where computational requirement is minimized by rendering and storing precomputed views. In both the cases, we show that our method allows a reduction in storage by almost 5× with no significant performance loss.

Abstract

In this paper, we present a solution to generate semantically richer descriptions and instructions for driver assistance and safety. Our solution builds upon a set of computer vision and machine learning modules. We start with low-level image processing and finally generate high-level descriptions. We do this by combining the results of the image pattern recognition module with the prior knowledge on traffic rules and larger context present in the video sequence. For recognition of road markings, we use a SVM based classifier and HOG based classifier. We test our method on real data captured in urban settings, and report impressive performance. Qualitative and quantitative performance of various modules are presented.

Abstract

Proteins display characteristic dynamical signatures that appear to be universal across all proteins regardless of topology and size. Here, we systematically characterize the universal features of fast side chain motions in proteins by examining the conformational energy surfaces of individual residues obtained using enhanced sampling molecular dynamics simulation (618 free energy surfaces obtained from 0.94 μs MD simulation). The side chain conformational free energy surfaces obtained using the adaptive biasing force (ABF) method for a set of eight proteins with different molecular weights and secondary structures are used to determine the methyl axial NMR order parameters (Oaxis 2), populations of side chain rotamer states (ρ), conformational entropies (Sconf), probability fluxes, and activation energies for side chain interrotameric transitions. The free energy barriers separating side chain rotamer states range from 0.3 to 12 kcal/mol in all proteins and follow a trimodal distribution with an intense peak at∼ 5 kcal/mol and two shoulders at∼ 3 and∼ 7.5 kcal/mol, indicating that some barriers are more favored than others by proteins to maintain a balance between their conformational stability and flexibility. The origin and the influences of the trimodal barrier distribution on the distribution of Oaxis 2 and the side chain conformational entropy are discussed. A hierarchical grading of rotamer states based on the conformational free energy barriers, entropy, and probability flux reveals three distinct classes of side chains in proteins. A unique nonlinear correlation is established between Oaxis 2 and the side chain rotamer populations (ρ). The …

Abstract

In banner advertisement scenario, advertiser expects his advertisement should be displayed to certain percentage of visitors. He also expects the advertisement to be relevant to the content of web page. On the other hand, to generate more revenue for a given website, publisher has to meet the coverage demands of several advertisers by providing appropriate sets of pages. Coverage patterns (CPs) are a set of web pages visited by certain percentage of visitors. The model of CPs does not reflect the real world scenario as they do not capture the aspect of relevance of web pages to the advertisement. In this paper, we propose a model of content-specific CPs and a methodology to extract content-specific CPs from click-through data, given keywords describing every web page. Content-specific CP is a set of web pages visited by certain percentage of users interested in particular content of the web pages. Experimental results show that the proposed model extracts CPs relevant to the topics of interest of advertiser over the previous model.

Abstract

The process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns

Abstract

Protein-protein interaction (PPI) networks are valuable biological source of data which contain rich information useful for protein function prediction. The PPI networks face data quality challenges like noise in the form of false positive edges and incompleteness in the form of missing biologically valued edges. These issues can be handled by enhancing data quality through graph transformations for improved protein function prediction. We proposed an improved method to extract similar proteins based on the notion of relaxed neighborhood. The proposed method can be applied to carry out graph transformation of PPI network data sets to improve the performance of protein function prediction task by adding biologically important protein interactions, removing dissimilar interactions and increasing reliability score of the interactions. By preprocessing PPI network data sets with the proposed methodology, experiment results on both un-weighted and weighted PPI network data sets show that, the proposed methodology enhances the data quality and improves prediction accuracy over other approaches. The results indicate that the proposed approach could utilize underutilized knowledge, such as distant relationships embedded in the PPI graph.

Abstract

Entity Disambiguation is the task of associating entity name mentions in text to the correct referent entities in the knowledge base, with the goal of understanding and extracting useful information from the document. Entity disambiguation is a critical component of systems designed to harness information shared by users on microblogging sites like Twitter. However, noise and lack of context in tweets makes disambiguation a difficult task. In this paper, we describe an Entity Disambiguation system, EDIUM, which uses User interest Models to disambiguate the entities in the user’s tweets. Our system jointly models the user’s interest scores and the context disambiguation scores, thus compensating the sparse context in the tweets for a given user. We evaluated the system’s entity linking capabilities on tweets from multiple users and showed that improvement can be achieved by combining the user models and the context based models.

Abstract

The last two decades have witnessed an exponential rise in web content from a plethora of domains, which has necessitated the use of domain-specific search engines. Diversity of crawled content is one of the crucial aspects of a domain-specific search engine. To a large extent, diversity is governed by the initial set of seed URLs. Most of the existing approaches rely on manual effort for seed selection. In this work we automate this process using URLs posted on Twitter. We propose an algorithm to get a set of diverse seed URLs from a Twitter URL graph. We compare the performance of our approach against the baseline zero similarity seed selection method and find that our approach beats the baseline by a significant margin.

Abstract

The velocity, volume and variety with which Twitter generates text is increasing exponentially. It is critical to determine latent sub-topics from such tweet data at any given point of time for providing better topic-wise search results relevant to users’ informational needs. The two main challenges in mining sub-topics from tweets in real-time are (1) understanding the semantic and the conceptual representation of the tweets, and (2) the ability to determine when a new sub-topic (or cluster) appears in the tweet stream. We address these challenges by proposing two unsupervised clustering approaches. In the first approach, we generate a semantic space representation for each tweet by keyword expansion and keyphrase identification. In the second approach, we transform each tweet into a conceptual space that represents the latent concepts of the tweet. We empirically show that the proposed methods outperform the state-of-the-art methods.