Abstract

Exhaustive pairwise matching on large datasets presents serious practical challenges, and has mostly remained an unexplored domain. We make a step in this direction by demonstrating the feasibility of scalable indexing and fast retrieval of appearance and geometric information in images. We identify unification of database filtering and geometric verification steps as a key step for doing this. We devise a novel inverted indexing scheme, based on Bloom filters, to scalably index high order features extracted from pairs of nearby features. Unlike a conventional inverted index, we can adapt the size of the inverted index to maintain adequate sparsity of the posting lists. This ensures constant time query retrievals. We are thus able to implement an exhaustive pairwise matching scheme, with linear time complexity, using the ‘query each image in turn’ technique. We find the exhaustive nature of our approach to be very useful in mining small clusters of images, as demonstrated by a 73.2% recall on the UKBench dataset. In the Oxford Buildings dataset, we are able to discover all the query buildings. We also discover interesting overlapping images connecting distant images.

Abstract

Image analysis tasks such as classification, clustering, detection, and retrieval are only as good as the feature representation of the images they use. Much research in computer vision is focused on finding better or semantically richer image representations. Bag of visual Words (BoW) is a representation that has emerged as an effective one for a variety of computer vision tasks. BoW methods traditionally use low level features. We have devised a strategy to use these low level features to create “higher level” features by making use of the spatial context in images. In this paper, we propose a novel hierarchical feature learning framework that uses a Naive Bayes Clustering algorithm to convert a 2-D symbolic image at one level to a 2-D symbolic image at the next level with richer features. On two popular datasets, Pascal VOC 2007 and Caltech 101, we empirically show that classification accuracy obtained from the hierarchical features computed using our approach is significantly higher than the traditional SIFT based BoW representation of images even though our image representations are more compact.

Abstract

. Category based image search, where the goal is to retrieve images of a specific category from a large database, is becoming increasingly popular. In such a setting, the query is often a classifier. However, the complexity of the classifiers (often SVMs) used for this purpose hinders the use of such a solution in practice. Problem becomes paramount when the database is huge and/or the dimensionality of the feature representation is also very large. In this paper, we address this issue by proposing a novel method which decomposes the query classifier into set of known eigen queries. We use their precomputed results (or scores) for computing the ranked list corresponding to novel queries. We also propose an approximate algorithm which accesses only a fraction of the data to perform fast retrieval. Experiments on various datasets show that our method reports high accuracy and efficiency. Apart from retrieval, the proposed method can also be used to discover interesting new concepts from the given dataset.

Abstract

. In this paper, we propose the canonical correlation kernel (CCK), that seamlessly integrates the advantages of lower dimensional representation of videos with a discriminative classifier like SVM. In the process of defining the kernel, we learn a low-dimensional (linear as well as nonlinear) representation of the video data, which is originally represented as a tensor. We densely compute features at single (or two) frame level, and avoid any explicit tracking. Tensor representation provides the holistic view of the video data, which is the starting point of computing the CCK. Our kernel is defined in terms of the principal angles between the lower dimensional representations of the tensor, and captures the similarity of two videos in an efficient manner. We test our approach on four public data sets and demonstrate consistent superior results over the state of the art methods, including those that use canonical correlations.

Abstract

Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that employs Partial Least Squares (PLS) regression to derive a measure of similarity between two tensors (videos). We demonstrate the use of this tensor similarity measure along with SVM classifiers to solve the tasks of hand gesture recognition and action classification. We show that our methods significantly outperform the state of the art approaches on two popular datasets: Cambridge hand gesture dataset and UCF sports action dataset. Our method requires no parameter tuning.

Abstract

In this paper, we propose a recognition scheme for the Indian script of Devanagari. Recognition accuracy of Devanagari script is not yet comparable to its Roman counterparts. This is mainly due to the complexity of the script, writing style etc. Our solution uses a Recurrent Neural Network known as Bidirectional LongShort Term Memory (BLSTM). Our approach does not require word to character segmentation, which is one of the most common reason for high word error rate. We report a reduction of more than 20% in word error rate and over 9% reduction in character error rate while comparing with the best available OCR system.

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