A compact representation of human actions by sliding coordinate coding

Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of visual words using a simple but very compact method called sliding coordinates coding (SCC). The SCC vector of each kind of word is only an eight-dimensional vector which is more compact than many of the spatial or spatial–temporal pooling methods in the literature. Our key observation is that the relative position is robust to the variations of video scale and view angle. Additionally, we design a temporal cutting scheme to define the margin of coding within video clips, since visual words far away from each other have little relationship. In experiments, four action data sets, including KTH, Rochester Activities, IXMAS, and UCF YouTube, are used for performance evaluation. Results show that our method achieves comparable or better performance than the state of the art, while using more compact and less complex models.Published versio

Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data

An efficient min-Hash based algorithm for discovery of dependencies in sparse high-dimensional data is presented. The dependencies are represented by sets of features co-occurring with high probability and are called co-ocsets. Sparse high dimensional descriptors, such as bag of words, have been proven very effective in the domain of image retrieval. To maintain high efficiency even for very large data collection, features are assumed independent. We show experimentally that co-ocsets are not rare, i.e. the independence assumption is often violated, and that they may ruin retrieval performance if present in the query image. Two methods for managing co-ocsets in such cases are proposed. Both methods significantly outperform the state-of-the-art in image retrieval, one is also significantly faster

Content based video retrieval via spatial-temporal information discovery.

Content based video retrieval (CBVR) has been strongly motivated by a variety of realworld applications. Most state-of-the-art CBVR systems are built based on Bag-of-visual- Words (BovW) framework for visual resources representation and access. The framework, however, ignores spatial and temporal information contained in videos, which plays a fundamental role in unveiling semantic meanings. The information includes not only the spatial layout of visual content on a still frame (image), but also temporal changes across the sequential frames. Specially, spatially and temporally co-occurring visual words, which are extracted under the BovW framework, often tend to collaboratively represent objects, scenes, or events in the videos. The spatial and temporal information discovery would be useful to advance the CBVR technology. In this thesis, we propose to explore and analyse the spatial and temporal information from a new perspective: i) co-occurrence of the visual words is formulated as a correlation matrix, ii) spatial proximity and temporal coherence are analytically and empirically studied to re ne this correlation. Following this, a quantitative spatial and temporal correlation (STC) model is de ned. The STC discovered from either the query example (denoted by QC) or the data collection (denoted by DC) are assumed to determine speci- city of the visual words in the retrieval model, i:e: selected Words-Of-Interest are found more important for certain topics. Based on this hypothesis, we utilized the STC matrix to establish a novel visual content similarity measurement method and a query reformulation scheme for the retrieval model. Additionally, the STC also characterizes the context of the visual words, and accordingly a STC-Based context similarity measurement is proposed to detect the synonymous visual words. The method partially solves an inherent error of visual vocabulary under the BovW framework. Systematic experimental evaluations on public TRECVID and CC WEB VIDEO video collections demonstrate that the proposed methods based on the STC can substantially improve retrieval e ectiveness of the BovW framework. The retrieval model based on STC outperforms state-of-the-art CBVR methods on the data collections without storage and computational expense. Furthermore, the rebuilt visual vocabulary in this thesis is more compact and e ective. Above methods can be incorporated together for e ective and e cient CBVR system implementation. Based on the experimental results, it is concluded that the spatial-temporal correlation e ectively approximates the semantical correlation. This discovered correlation approximation can be utilized for both visual content representation and similarity measurement, which are key issues for CBVR technology development

Mining Mid-level Features for Image Classification

International audienceMid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In par- ticular, we mine relevant patterns of local compositions of densely sampled low-level features. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During this process, we pay special attention to keeping all the local histogram information and to selecting the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and an extension to exploit both local and global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the-art results on various image classification benchmarks, including Pascal VOC

Human action classification based on sequential bag-of-words model

Recently, approaches utilizing spatial-temporal features have achieved great success in human action classification. However, they typically rely on bag-of-words (BoWs) model, and ignore the spatial and temporal structure information of visual words, bringing ambiguities among similar actions. In this paper, we present a novel approach called sequential BoWs for efficient human action classification. It captures temporal sequential structure by segmenting the entire action into sub-actions. Each sub-action has a tiny movement within a narrow range of action. Then the sequential BoWs are created, in which each sub-action is assigned with a certain weight and salience to highlight the distinguishing sections. It is noted that the weight and salience are figured out in advance according to the sub-action's discrimination evaluated by training data. Finally, those sub-actions are used for classification respectively, and voting for united result. Experiments are conducted on UT-interaction dataset and Rochester dataset. The results show its higher robustness and accuracy over most state-of-the-art classification approaches. ? 2014 IEEE.EI2280-228

Higher-order Occurrence Pooling on Mid- and Low-level Features: Visual Concept Detection

In object recognition, the Bag-of-Words model assumes: i) extraction of local descriptors from images, ii) embedding these descriptors by a coder to a given visual vocabulary space which results in so-called mid-level features, iii) extracting statistics from mid-level features with a pooling operator that aggregates occurrences of visual words in images into so-called signatures. As the last step aggregates only occurrences of visual words, it is called as First-order Occurrence Pooling. This paper investigates higher-order approaches. We propose to aggregate over co-occurrences of visual words, derive Bag-of-Words with Second- and Higher-order Occurrence Pooling based on linearisation of so-called Minor Polynomial Kernel, and extend this model to work with adequate pooling operators. For bi- and multi-modal coding, a novel higher-order fusion is derived. We show that the well-known Spatial Pyramid Matching and related methods constitute its special cases. Moreover, we propose Third-order Occurrence Pooling directly on local image descriptors and a novel pooling operator that removes undesired correlation from the image signatures. Finally, Uni- and Bi-modal First-, Second-, and Third-order Occurrence Pooling are evaluated given various coders and pooling operators. The proposed methods are compared to other approaches (e.g. Fisher Vector Encoding) in the same testbed and attain state-of-the-art results

Generic decoding of seen and imagined objects using hierarchical visual features

Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding of seen and imagined contents, the prediction is limited to training examples. We present a decoding approach for arbitrary objects, using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features including those from a convolutional neural network can be predicted from fMRI patterns and that greater accuracy is achieved for low/high-level features with lower/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, the decoding of imagined objects reveals progressive recruitment of higher to lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval

Learning directional co-occurrence for human action classification

Spatio-temporal interest point (STIP) based methods have shown promising results for human action classification. However, state-of-art works typically utilize bag-of-visual words (BoVW), which focuses on the statistical distribution of features but ignores their inherent structural relationships. To solve this problem, a descriptor, namely directional pairwise feature (DPF), is proposed to encode the mutual direction information between pairwise words, aiming at adding more spatial discriminant to BoVW. Firstly, STIP features are extracted and classified into a set of labeled words. Then in each frame, the DPF is constructed for every pair of words with different labels, according to their assigned directional vector. Finally, DPFs are quantized to be a probability histogram as a representation of human action. The proposed method is evaluated on two challenging datasets, Rochester and UT-interaction, and the results based on chi-squared kernel SVM classifiers confirm that our method can classify human actions with high accuracies.AcousticsEngineering, Electrical & ElectronicEICPCI-S(ISTP)

Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images

Abstract—We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. In this paper, a novel framework based on Bayesian joint topic modelling is proposed, which differs significantly from the existing ones in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple object co-existence so that “explaining away ” inference can resolve ambiguity and lead to better learning and localisation. (2) Image backgrounds are shared across classes to better learn varying surroundings and “push out ” objects of interest. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Moreover, the Bayesian formulation enables the exploitation of various types of prior knowledge to compensate for the limited supervision offered by weakly labelled data, as well as Bayesian domain adaptation for transfer learning. Extensive experiments on the PASCAL VOC, ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of our Bayesian joint model for weakly supervised object localisation