Abstract

Frequent Itemset Mining (FISM) attempts to find large and frequent itemsets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent (mixture property) and (ii) only a subset of items related to a customer intent are present in most market baskets (projection property). We propose a simple and robust framework called LOGICAL ITEMSET MINING (LISM) that treats each market basket as a mixture-of, projections-of, latent customer intents. LISM attempts to discover logical itemsets from such bagof-items data. Each logical itemset can be interpreted as a latent customer intent in retail or semantic concept in text tagsets. While the mixture and projection properties are easy to appreciate in retail domain, they are present in almost all types of bag-of-items data. Through experiments on two large datasets, we demonstrate the quality, novelty, and actionability of logical itemsets discovered by the simple, scalable, and aggressively noise-robust LISM framework. We conclude that while FISM discovers a large number of noisy, observed, and frequent itemsets, LISM discovers a small number of high quality, latent logical itemsets.

Abstract

MRF models have shown state-of-the-art performance for many computer vision tasks. In this work, we propose a non-local MRF model for image completion problem. The goal of image completion is to fill user specified “target” region with patches of “source” regions in a way that is visually plausible to an observer. We represent the patches in the target region of the image as random variables in an MRF, and introduce a novel energy function on these variables. Each variable takes a label from a label set which is a collection of patches of the source region. The quality of the image completion is determined by the value of the energy function. The non-locality in the MRF is achieved through long range pairwise potentials. These long range pairwise potentials are defined to capture the inherent repeating patterns present in heritage architectural images. We minimize this energy function using Belief Propagation to obtain globally optimal image completion. We have tested our method on a wide variety of images and shown superior performance over previously published results for this task.

Abstract

Constructing global image representations from local fea-ture descriptors is a common step in most visual classifica-tion tasks. Traditionally, the Bag of Features (BoF) rep-resentations involving hard vector quantization have been used ubiquitously for such tasks. Recent works have demon-strated superior performance of soft assignments over hard assignments. Fisher vector representations have been shown to outperform other global representations on most bench-mark datasets. Fisher vectors (i) use soft assignments, and(ii) reduce information loss due to quantization by captur-ing the deviations from the mean. However, the Fisher vec-tor representations are huge and the representation size in-creases linearly with the vocabulary size. Recent findings report that the classification performance of Fisher vectors is proportional to the vocabulary size. Computational and storage requirements, however, discourage the use of arbi-trarily large vocabularies. Also, Fisher vectors are not in-herently discriminative. In this paper, we devise a novel strategy to compute sparse Fisher representations. This al-lows us to increase the vocabulary size with little computa-tion and storage overhead and still attain the performance ofa larger vocabulary. Further, we describe an approach to en-code class-discriminative information in the Fisher vectors.We evaluate our method on four popular datasets. Empir-ical results show that our representations consistently out-perform the traditional Fisher Vector representations andare comparable to the state of art approaches.

Abstract

Text line segmentation is a basic step in any OCRsys-tem. Its failure deteriorates the performance of OCRen-gines. This is especially true for the Indian languages due to the nature of scripts. Many segmentation algorithms are proposed in literature. Often these algorithms fail to adapt dynamically to a given page and thus tend to yield poor seg-mentation for some specific regions or some specific pages. In this work we design a text line segmentation post processor which automatically localizes and corrects the segmentation errors. The proposed segmentation post processor, which works in a “learning by examples” framework, is not only independent to segmentation algorithms but also robust to the diversity of scanned pages.We show over 5% improvement in text line segmentation on a large dataset of scanned pages for multiple Indian lan-guages

Abstract

Instance retrieval has emerged as a promising research area with buildings as the popular test subject. Given a query im-age or region, the objective is to find images in the database containing the same object or scene. There has been a recents urge in efforts in finding instances of the same building in challenging datasets such as the Oxford 5k dataset[19], Ox-ford 100k dataset and the Paris dataset[20].We ascend one level higher and pose the question:Are Buildings Only Instances?Buildings located in the same geographical region or constructed in a certain time period in history often follow a specific method of construction.These architectural styles are characterized by certain fea-tures which distinguish them from other styles of architec-ture. We explore, beyond the idea of buildings as instances,the possibility that buildings can be categorized based on the architectural style. Certain characteristic features distin-guish an architectural style from others. We perform exper-iments to evaluate how characteristic information obtained from low-level feature configurations can help in classifica-tion of buildings into architectural style categories. Encour-aged by our observations, we mine characteristic features with semantic utility for different architectural styles from our dataset of European monuments. These mined features are of various scales, and provide an insight into what makes a particular architectural style category distinct. The utility of the mined characteristics is verified from Wikipedia

Abstract

In this paper, we propose a framework for content level ac-cess to the scanned pages of Digital Library of India (DLI).The current Optical Character Recognition (OCR) systems are not robust and reliable enough for generating accurate text from DLI pages. We propose a search scheme which fuses noisy OCR output and holistic visual features for con-tent level access to the DLI pages. Visual content is cap-tured using Bag of Visual Words (BoVW) approach. We show that our fusion scheme improves over the individual methods in terms of mean Average Precision (mAP) and mean precision at 10 (mPrec@10). We exploit the fact that OCR has a high precision while BoVW has a high recall.We use a modified edit distance to improve the order of re-sults ranked by BoVW. Experiments are carried out on largedatasets of DLI pages in Hindi and Telugu languages. Wevalidate our method on more than 10,000 pages and 4 Mil-lion words, and report a mAP of around 0.8 and mPrec@10of more than 0.9. We show improvements over BoVW by introducing query expansion. We also demonstrate a textual query interface for the search system.

Abstract

A wide category of objects and scenes can be effectively searched and classified using the modern descriptors and classifiers. With the performance on many popular cate-gories becoming satisfactory, we explore into the issues as-sociated with much harder recognition problems.We address the problem of searching specific images in In-dian stone-carvings and sculptures in an unsupervised set-up. For this, we introduce a new dataset of 524 images containing sculptures and carvings of eight different Indian deities and three other subjects popular in the Indian sce-nario. We perform a thorough analysis to investigate var-ious challenges associated with this task. A new image-representation is proposed using a sequence of discriminative patches mined in an unsupervised manner. For each image,these patches are identified based on their ability to distin-guish the given image from the image most dissimilar to it. Then a rejection-based re-ranking scheme is formulated based on both similarity as well as dissimilarity between two images. This new scheme is experimentally compared with two baselines using state-of-the-art descriptors on the pro-posed dataset. Empirical evaluations demonstrate that our proposed method of image-representation and rejection cas-cade improves the retrieval performance on this hard prob-lem as compared to the baseline descriptors.

Abstract

n this paper, we demonstrate a computer vision applica-tion on mobile phones. One can take a picture at a her-itage site/monument and obtain associated annotations on a mid-end mobile phone instantly. This does not require any communication of images or features with a remote server,and all the necessary computations take place on the phone itself. We demonstrate the app on two Indian heritage sites:Golkonda Fort and Hampi Temples. Underlying our appli-cation, we have a Bag of visual Words (BoW) image retrieval system, and an annotated database of images.In the process of developing this mobile app, we extend the performance, scope and applicability of computer vision techniques: (i) we do a BoW-based image retrieval on mo-bile phones from a database of 10K images within 50 MB of storage and 10 MB of RAM. (ii) we introduce a vocabulary pruning method for reducing the vocabulary size. (iii) wede sign a simple method of database pruning, that helps in reducing the size of the inverted index by removing seman-tically similar images. In (ii) and (iii), we demonstrate how memory(RAM) and computational speed can be optimized without any loss in performance.

Abstract

Motion segmentation is an inevitable component for mobile robotic systems such as the case with robots performing SLAM and collision avoidance in dynamic worlds. This paper proposes an incremental motion segmentation system that efficiently segments multiple moving objects and simultaneously build the map of the environment using visual SLAM modules. Multiple cues based on optical flow and two view geometry are integrated to achieve this segmentation. A dense optical flow algorithm is used for dense tracking of features. Motion potentials based on geometry are computed for each of these dense tracks. These geometric potentials along with the optical flow potentials are used to form a graph like structure. A graph based segmentation algorithm then clusters together nodes of similar potentials to form the eventual motion segments. Experimental results of high quality segmentation on different publicly available datasets demonstrate the effectiveness of our method.

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.