Abstract

Automatic annotation of an audio or a music piece with multiple labels helps in understanding the composition of a music. Such meta-level information can be very useful in applications such as music transcription, retrieval, organization and personalization. In this work, we formulate the problem of annotation as multi-label classification which is considerably different from that of a popular single (binary or multi-class) label classification. We employ both the nearest neighbour and max-margin (SVM) formulations for the automatic annotation. We consider K-NN and SVM that are adapted for multi-label classification using one-vs-rest strategy and a direct multi-label classification formulation using ML-KNN and M3L. In the case of music, often the signatures of the labels (e.g. instruments and vocal signatures) are fused in the features. We therefore propose a simple feature augmentation technique based on non …

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

With a view to provide a user-friendly interface for designing, training and developing deep learning frameworks, we have developed Expresso, a GUI tool written in Python. Expresso is built atop Caffe, the open-source, prize-winning framework popularly used to develop Convolutional Neural Networks. Expresso provides a convenient wizard-like graphical interface which guides the user through various common scenarios--data import, construction and training of deep networks, performing various experiments, analyzing and visualizing the results of these experiments. The multi-threaded nature of Expresso enables concurrent execution and notification of events related to the aforementioned scenarios. The GUI sub-components and inter-component interfaces in Expresso have been designed with extensibility in mind. We believe Expresso's flexibility and ease of use will come in handy to researchers, newcomers and seasoned alike, in their explorations related to deep learning.

Abstract

We propose a fast and accurate method for counting themitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integratedmanner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns amis classification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest)and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4%improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge

Abstract

Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are included for benchmarking.

Abstract

Social commerce is a field that is growing rapidly with the rise of Web 2.0 technologies. This paper presents a review of existing research on this topic to ensure a comprehensive understanding of social commerce. First, we explore the evolution of social commerce from its marketing origins. Next, we examine various definitions of social commerce and the motivations behind it. We also investigate its advantages and disadvantages for both businesses and customers. Then, we explore two major tools for important for social commerce: Sentiment Analysis, and Social Network Analysis. By delving into well-known research papers in Information Retrieval and Complex Networks, we seek to present a survey of current research in multifarious aspects of social commerce to the scientific research community.

Abstract

In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations. In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA), an extension of CCA, for learning shared subspaces taking into account high level semantic information in the form of multi-label annotations. Unlike CCA, ml-CCA does not rely on explicit pairing between modalities, instead it uses the multi-label information to establish correspondences. This results in a discriminative subspace which is better suited for cross-modal retrieval tasks. We also present Fast ml-CCA, a computationally efficient version of ml-CCA, which is able to handle large scale datasets. We show the efficacy of our approach by conducting extensive cross-modal retrieval experiments on three standard benchmark datasets. The results show that the proposed approach achieves state of the art retrieval performance on the three datasets.

Abstract

Localization in 3D is an important problem with wide ranging applications from autonomous navigation in robotics to location specific services on mobile devices. GPS sensors are a commercially viable option for localization, and are ubiquitous in their use, especially in portable de-vices. With the proliferation of mobile cameras however, maturing localization algorithms based on computer vision are emerging as a viable alternative. Although both vision and GPS based localization algorithms have many limita-tions and inaccuracies, there are some interesting compli-mentarities in their success/failure scenarios that justify an investigation into their joint utilization. Such investigations are further justified considering that many of the modern wearable and mobile computing devices come with sensors for both GPS and vision. In this work, we investigate approaches to reinforce GPS localization with vision algorithms and vice versa. Specif-ically, we show how noisy GPS signals can be rectified by vision based localization of images captured in the vicin-ity. Alternatively, we also show how GPS readouts might be used to disambiguate images when they are visually similar looking but belong to different places. Finally, we empiri-cally validate our solutions to show that fusing both these approaches can result in a more accurate and reliable lo-calization of videos captured with a Contour action camera, over a 600 meter long path, over 10 different days.

Abstract

on the path are shown in Figure 6;(a) O1 from a;(b) O1 from b;(c) O1 from c;(d) O1 from d; and (e) O2 from e.(f) O2 from f.

Abstract

Various projections or views of a heterogeneous information network can be modeled using the graph OLAP (On-line Analytical Processing) framework for effective decision making. Detecting anomalous projections of the network can help the analysts identify regions of interest from the graph specific to the projection attribute. While most previous studies on outlier detection in graphs deal with outlier nodes, edges or subgraphs, we are the first to propose detection of graph cuboid outliers. Further we perform this detection in a query sensitive way. Given a general subgraph query on a heterogeneous network, we study the problem of finding outlier cuboids from the graph OLAP lattice. A Graph Cuboid Outlier (GCOutlier) is a cuboid with exceptionally high density of matches for the query. The GCOutlier detection task is clearly challenging because: (1) finding matches for the query (subgraph isomorphism) is NP-hard; (2) number of matches for the query can be very high; and (3) number of cuboids can be large. We provide an approximate solution to the problem by computing only a fraction of the total matches originating from a select set of candidate nodes and including a select set of edges, chosen smartly. We perform extensive experiments on synthetic datasets to showcase the execution time versus accuracy trade-off. Experiments on real datasets like Four Area and Delicious containing thousands of nodes reveal interesting GCOutliers.