Implicit and Explicit Concept Relations in Deep Neural Networks for Multi-Label Video/Image Annotation

In this paper, we propose a deep convolutional neural network (DCNN) architecture that addresses the problem of video/image concept annotation by exploiting concept relations at two different levels. At the first level, we build on ideas from multi-task learning, and propose an approach to learn concept-specific representations that are sparse, linear combinations of representations of latent concepts. By enforcing the sharing of the latent concept representations, we exploit the implicit relations between the target concepts. At a second level, we build on ideas from structured output learning and propose the introduction, at training time, of a new cost term that explicitly models the correlations between the concepts. By doing so, we explicitly model the structure in the output space (i.e., the concept labels). Both of the above are implemented using standard convolutional layers and are incorporated in a single DCNN architecture that can then be trained end-to-end with standard back-propagation. Experiments on four large-scale video and image data sets show that the proposed DCNN improves concept annotation accuracy and outperforms the related state-of-the-art methods.

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abstract: As an important procedure in image retrieval, off-line indexing focuses on organizing relevant images together and making them easy to access. However, most of existing indexing strategies view database images individually and only consider partial relevance, i.e., either visual or semantic relevance among them. To overcome these issues and design better indexing strategy, we propose to package semantically relevant images into superimages, and then index superimages instead of single images. Superimage effectively packages multiple images into one new unit, hence significantly decreases the number of images to be indexed. This naturally saves the memory cost and retrieval time. To make the final index file discriminative to both visual and semantic relevances, we extract local descriptors from superimages and index them with inverted file. During online retrieval, we only need to extract local descriptors from queries, but could get semantic-aware retrieval results. This is because during our off-line indexing stage, both the semantically and visually relevant images are organized together. Therefore, our approach is superior to many online retrieval fusion algorithms. Experimental results on UKbench, Holidays, and one large-scale dataset all manifest the promising performance of our approach, i.e., competitive precision, better efficiency, and only about 1/2 memory consumption compared with state-of-the-arts.

title: New Graph Structured Sparsity Model for Multi-label Image Annotations
abstract: In multi-label image annotations, because each image is associated to multiple categories, the semantic terms (label classes) are not mutually exclusive. Previous research showed that such label correlations can largely boost the annotation accuracy. However, all existing methods only directly apply the label correlation matrix to enhance the label inference and assignment without further learning the structural information among classes. In this paper, we model the label correlations using the relational graph, and propose a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications. As a result, our new method will capture and utilize the hidden class structures in relational graph to improve the annotation results. In proposed objective, a large number of structured sparsity-inducing norms are utilized, thus the optimization becomes difficult. To solve this problem, we derive an efficient optimization algorithm with proved convergence. We perform extensive experiments on six multi-label image annotation benchmark data sets. In all empirical results, our new method shows better annotation results than the state-of-the-art approaches.

title: Max-Margin Markov Networks
abstract: In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M3 networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition and collective hypertext classification demonstrate very significant gains over previous approaches.

title: Multi-label classification with Bayesian network-based chain classifiers
abstract: We introduce a novel method for chaining Bayesian classifiers.We provide a detailed analysis of the proposed method.We perform an extensive empirical evaluation.We show very competitive results against related approaches. In multi-label classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of labels (label power-set methods) or by building independent classifiers for each class (binary relevance methods). The first approach suffers from high computationally complexity, while the second approach ignores possible dependencies among classes. Chain classifiers have been recently proposed to address these problems, where each classifier in the chain learns and predicts the label of one class given the attributes and all the predictions of the previous classifiers in the chain. In this paper we introduce a method for chaining Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multi-label classification. A Bayesian network is induced from data to: (i) represent the probabilistic dependency relationships between classes, (ii) constrain the number of class variables used in the chain classifier by considering conditional independence conditions, and (iii) reduce the number of possible chain orders. The effects in the Bayesian chain classifier performance of considering different chain orders, training strategies, number of class variables added in the base classifiers, and different base classifiers, are experimentally assessed. In particular, it is shown that a random chain order considering the constraints imposed by a Bayesian network with a simple tree-based structure can have very competitive results in terms of predictive performance and time complexity against related state-of-the-art approaches.

title: Fully Connected Deep Structured Networks
abstract: Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.

title: Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
abstract: Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.

title: Conditional Random Fields as Recurrent Neural Networks
abstract: Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate Conditional Random Fields with Gaussian pairwise potentials and mean-field approximate inference as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.

title: Joint Structural Learning to Rank with Deep Linear Feature Learning
abstract: Multimedia information retrieval usually involves two key modules including effective feature representation and ranking model construction. Most existing approaches are incapable of well modeling the inherent correlations and interactions between them, resulting in the loss of the latent consensus structure information. To alleviate this problem, we propose a learning to rank approach that simultaneously obtains a set of deep linear features and constructs structure-aware ranking models in a joint learning framework. Specifically, the deep linear feature learning corresponds to a series of matrix factorization tasks in a hierarchical manner, while the learning-to-rank part concentrates on building a ranking model that effectively encodes the intrinsic ranking information by structural SVM learning. Through a joint learning mechanism, the two parts are mutually reinforced in our approach, and meanwhile their underlying interaction relationships are implicitly reflected by solving an alternating optimization problem. Due to the intrinsic correlations among different queries (i.e., similar queries for similar ranking lists), we further formulate the learning-to-rank problem as a multi-task problem, which is associated with a set of mutually related query-specific learning-to-rank subproblems. For computational efficiency and scalability, we design a MapReduce-based parallelization approach to speed up the learning processes. Experimental results demonstrate the efficiency, effectiveness, and scalability of the proposed approach in multimedia information retrieval.

title: How transferable are features in deep neural networks?
abstract: Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.

title: Deep Multi-task Representation Learning: A Tensor Factorisation Approach
abstract: Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.

title: Multi-source Deep Learning for Human Pose Estimation
abstract: Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. The task for estimating body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms state-of-the-art by up to 8.6 percent on three public benchmark datasets.

title: Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization
abstract: Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challenge. Existing cross-media retrieval methods usually focus on modeling the pairwise correlation or semantic information separately. In fact, these two kinds of information are complementary to each other and optimizing them simultaneously can further improve the accuracy. In this paper, we propose a novel feature learning algorithm for cross-media data, called joint representation learning (JRL), which is able to explore jointly the correlation and semantic information in a unified optimization framework. JRL integrates the sparse and semisupervised regularization for different media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type. On one hand, JRL learns sparse projection matrix for different media simultaneously, so different media can align with each other, which is robust to the noise. On the other hand, both the labeled data and unlabeled data of different media types are explored. Unlabeled examples of different media types increase the diversity of training data and boost the performance of joint representation learning. Furthermore, JRL can not only reduce the dimension of the original features, but also incorporate the cross-media correlation into the final representation, which further improves the performance of both cross-media retrieval and single-media retrieval. Experiments on two datasets with up to five media types show the effectiveness of our proposed approach, as compared with the state-of-the-art methods.

title: Structured Domain Adaptation
abstract: In many real-world applications, labeled data are either expensive or too scarce to be used to train an accurate classifier. Therefore, it is worth exploring and often essential to make full use of existing resources. Domain adaptation is one of the most promising techniques of leveraging an existing well-labeled source domain and a limited labeled target domain. With the aim of better understanding new or unknown domains through well-labeled ones, this paper proposes an efficient and powerful algorithm, named structured domain adaptation (SDA), which transfers knowledge across two domains. Specifically, SDA aims to seek a discriminate subspace shared by two domains where the well-learned knowledge of the source domain can be transferred to the target domain. In SDA, samples from both domains are combined together to reveal more shared information across two domains. Furthermore, an iteratively structured matrix <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> is learned to bridge the domain shift so that the marginal and conditional distributions are mitigated. Since the target is labeled to a limited extent or even totally unlabeled, we adopt pseudolabels of the target data to optimize <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> iteratively so that a reconstruction coefficient matrix is learned in order to guarantee local awareness. Our approach is robust to outliers as it applies an <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula>-norm on the error term. SDA can work in either an unsupervised or a semisupervised manner. Extensive experiments on five data sets including faces, objects, digits, and visual events, demonstrate that SDA outperforms several state-of-the-art approaches with significant advantages. Notably, SDA can even achieve 100% accuracy on several popular benchmarks.

title: Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
abstract: Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial pyramid matching) contain much more local discriminative and structural information, which is implicitly embedded in the images. To further improve the performance, in this paper, we propose to combine CNN with dictionary-based models for scene recognition and visual domain adaptation (DA). Specifically, based on the well-tuned CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations are further constructed, namely, mid-level local representation (MLR) and convolutional Fisher vector (CFV) representation. In MLR, an efficient two-stage clustering method, i.e., weighted spatial and feature space spectral clustering on the parts of a single image followed by clustering all representative parts of all images, is used to generate a class-mixture or a class-specific part dictionary. After that, the part dictionary is used to operate with the multiscale image inputs for generating mid-level representation. In CFV, a multiscale and scale-proportional Gaussian mixture model training strategy is utilized to generate Fisher vectors based on the last convolutional layer of CNN. By integrating the complementary information of MLR, CFV, and the CNN features of the fully connected layer, the state-of-the-art performance can be achieved on scene recognition and DA problems. An interested finding is that our proposed hybrid representation (from VGG net trained on ImageNet) is also complementary to GoogLeNet and/or VGG-11 (trained on Place205) greatly.

title: Comparison of Fine-Tuning and Extension Strategies for Deep Convolutional Neural Networks
abstract: In this study we compare three different fine-tuning strategies in order to investigate the best way to transfer the parameters of popular deep convolutional neural networks that were trained for a visual annotation task on one dataset, to a new, considerably different dataset. We focus on the concept-based image/video annotation problem and use ImageNet as the source dataset, while the TRECVID SIN 2013 and PASCAL VOC-2012 classification datasets are used as the target datasets. A large set of experiments examines the effectiveness of three fine-tuning strategies on each of three different pre-trained DCNNs and each target dataset. The reported results give rise to guidelines for effectively fine-tuning a DCNN for concept-based visual annotation.

title: TRECVID 2015 - An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics
abstract: The TREC Video Retrieval Evaluation (TRECVID) 2011 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last ten years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID is funded by the National Institute of Standards and Technology (NIST) and other US government agencies. Many organizations and individuals worldwide contribute significant time and effort

title: Unified Video Annotation via Multigraph Learning
abstract: Learning-based video annotation is a promising approach to facilitating video retrieval and it can avoid the intensive labor costs of pure manual annotation. But it frequently encounters several difficulties, such as insufficiency of training data and the curse of dimensionality. In this paper, we propose a method named optimized multigraph-based semi-supervised learning (OMG-SSL), which aims to simultaneously tackle these difficulties in a unified scheme. We show that various crucial factors in video annotation, including multiple modalities, multiple distance functions, and temporal consistency, all correspond to different relationships among video units, and hence they can be represented by different graphs. Therefore, these factors can be simultaneously dealt with by learning with multiple graphs, namely, the proposed OMG-SSL approach. Different from the existing graph-based semi-supervised learning methods that only utilize one graph, OMG-SSL integrates multiple graphs into a regularization framework in order to sufficiently explore their complementation. We show that this scheme is equivalent to first fusing multiple graphs and then conducting semi-supervised learning on the fused graph. Through an optimization approach, it is able to assign suitable weights to the graphs. Furthermore, we show that the proposed method can be implemented through a computationally efficient iterative process. Extensive experiments on the TREC video retrieval evaluation (TRECVID) benchmark have demonstrated the effectiveness and efficiency of our proposed approach.

title: Image Annotation by Multiple-Instance Learning With Discriminative Feature Mapping and Selection
abstract: Multiple-instance learning (MIL) has been widely investigated in image annotation for its capability of exploring region-level visual information of images. Recent studies show that, by performing feature mapping, MIL can be cast to a single-instance learning problem and, thus, can be solved by traditional supervised learning methods. However, the approaches for feature mapping usually overlook the discriminative ability and the noises of the generated features. In this paper, we propose an MIL method with discriminative feature mapping and feature selection, aiming at solving this problem. Our method is able to explore both the positive and negative concept correlations. It can also select the effective features from a large and diverse set of low-level features for each concept under MIL settings. Experimental results and comparison with other methods demonstrate the effectiveness of our approach.

title: NUS-WIDE: a real-world web image database from National University of Singapore
abstract: This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 5x5 fixed grid partitions, and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation. Based on this dataset, we highlight characteristics of Web image collections and identify four research issues on web image annotation and retrieval. We also provide the baseline results for web image annotation by learning from the tags using the traditional k-NN algorithm. The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval.

title: ImageNet Large Scale Visual Recognition Challenge
abstract: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

title: A simple and efficient sampling method for estimating AP and NDCG
abstract: We consider the problem of large scale retrieval evaluation. Recently two methods based on random sampling were proposed as a solution to the extensive effort required to judge tens of thousands of documents. While the first method proposed by Aslam et al.  is quite accurate and efficient, it is overly complex, making it difficult to be used by the community, and while the second method proposed by Yilmaz et al., infAP , is relatively simple, it is less efficient than the former since it employs uniform random sampling from the set of complete judgments. Further, none of these methods provide confidence intervals on the estimated values.
 The contribution of this paper is threefold: (1) we derive confidence intervals for infAP, (2) we extend infAP to incorporate nonrandom relevance judgments by employing stratified random sampling, hence combining the efficiency of stratification with the simplicity of random sampling, (3) we describe how this approach can be utilized to estimate nDCG from incomplete judgments. We validate the proposed methods using TREC data and demonstrate that these new methods can be used to incorporate nonrandom samples, as were available in TREC Terabyte track '06.

title: Caffe: Convolutional Architecture for Fast Feature Embedding
abstract: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

title: Segmentation as selective search for object recognition
abstract: For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge.