Progressive Visual Object Detection with Positive Training Examples Only

A common obstacle in effective learning of visual concept classifiers is the scarcity of positive training examples due to expensive labeling cost. This paper explores the sampling of weakly tagged web images for concept learning without human assistance. In particular, ontology knowledge is incorporated for semantic pooling of positive examples from ontologically neighboring concepts. This effectively widens the coverage of the positive samples with visually more diversified content, which is important for learning a good concept classifier. We experiment with two learning strategies: aggregate and incremental. The former strategy re-trains a new classifier by combining existing and newly collected examples, while the latter updates the existing model using the new samples incrementally. Extensive experiments on NUS-WIDE and VOC 2010 datasets show very encouraging results, even when comparing with classifiers learnt using expert labeled training examples.

Amongst all the methods of machine learning, learning from examples is considered as a key to acquire knowledge automatically. Learning from examples is to obtain a general cover through induction from a given set of positive and negative examples of a concept, which may describe all the positive examples, but reject all the negative examples of that concept. Extension matrix theory, a branch of learning from examples, presents the training examples as two matrices which consist of positive and negative examples respectively, and then finds a path satisfied with a positive example against the background of negative examples. Obviously, such a path is a conjunction of conditions that is satisfied by the positive example. However, most of all of pattern recognition problems, such as handwritten Chinese character recognition, have an overlay area. It is necessary to solve the problem of nonlinear boundaries among different classes when machine learning is applied to acquire the recognition rules. An advanced extension matrix algorithm is proposed in this paper, in which a heuristic search based on the average entropy is used to get the approximate solutions of the minimal complex. A potential function is used to estimate the probability density function of the overlay area between positive and negative examples, so that the nonlinear interfaces of the interclass areas may be obtained. This algorithm is applied to supervised learning for Chinese character recognition to acquire recognition rules, and the implementations upon 751,000 loosely-constrained handwritten Chinese characters indicate that these methodologies can be applied to a practical recognition system with promising results.

An object detection method is developed based on combining top-down recognition with bottom-up image segmentation. There are three main steps in this method: a shape learning step, a hypothesis generation step and a verification step. In the shape learning step, training images are used to building codebook dictionary, and matching template is also constructed based on codebook dictionary. In the top-down hypothesis generation step, the Probability Shape Context (PSC) is used to generate a set of hypotheses of object locations. In the verification step, the hypotheses of object locations are segmented based on Partial Differential Equation (PDE). Based on segmented outlines and matching template, the inference is used to prune out false positives. Experimental results demonstrate that our proposed approach is able to accurately detect objects with only a few positive training examples.

Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.

Establishing structural or functional relationship between sequences, for instance to infer the structural class of an unannotated protein, is a key task in biological analysis. Protein sequences undergo complex transformations such as mutation, insertion and deletion during the evolutionary process and typically share low sequence similarity on the superfamily level, making the task for remote homology detection based on primary sequence only very challenging. Based on previous studies stating that knowledge based on only a subset of critical positions and the preferred symbols on such positions are sufficient for remote homology detection, we present a series of works, each enforcing different notion of sparsity, to recover such critical positions. We first start with a generative model and present the sparse profile hidden Markov models. Such generative approach recovers some critical patterns and motivates the need for discriminative learning. In our second study, we present a discriminative approach to recover such critical positions and the preferred symbols. In our third study, we address the issue of very few positive training examples, accompanied by a large number of negative training examples, which is typical in many remote homology detection task. Such issue motivates the need for semi-supervised learning. However, though containing abundant useful and critical information, large uncurated sequence databases also contain a lot of noise, which may compromise the quality of the classifiers. As a result, we present a systematic and biologically motivated framework for semi-supervised learning with large uncurated sequence databases. Combined with a very fast string kernel, our method not only realizes rapid and accurate remote homology detection and show state-of-the-art performance, but also recovers some critical patterns conserved in superfamilies.

BackgroundThe rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. Most existing approaches for the abbreviation definition identification task employ rule-based methods. While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns. Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem. However, they require manually labeled training data.MethodsIn this work, we develop a machine learning algorithm for abbreviation definition identification in text which makes use of what we term naturally labeled data. Positive training examples are naturally occurring potential abbreviation-definition pairs in text. Negative training examples are generated by randomly mixing potential abbreviations with unrelated potential definitions. The machine learner is trained to distinguish between these two sets of examples. Then, the learned feature weights are used to identify the abbreviation full form. This approach does not require manually labeled training data.ResultsWe evaluate the performance of our algorithm on the Ab3P, BIOADI and Medstract corpora. Our system demonstrated results that compare favourably to the existing Ab3P and BIOADI systems. We achieve an F-measure of 91.36% on Ab3P corpus, and an F-measure of 87.13% on BIOADI corpus which are superior to the results reported by Ab3P and BIOADI systems. Moreover, we outperform these systems in terms of recall, which is one of our goals.

The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database. This is a challenging task due to changes in viewpoint, imaging conditions and the large size of the image database. The contribution of this work is two-fold. First, we cast the place recognition problem as a classification task and use the available geotags to train a classifier for each location in the database in a similar manner to per-exemplar SVMs in object recognition. Second, as only few positive training examples are available for each location, we propose a new approach to calibrate all the per-location SVM classifiers using only the negative examples. The calibration we propose relies on a significance measure essentially equivalent to the p-values classically used in statistical hypothesis testing. Experiments are performed on a database of 25,000 geotagged street view images of Pittsburgh and demonstrate improved place recognition accuracy of the proposed approach over the previous work.

For hyper-rectangles in $$\mathbb{R}^{d}$$ Auer (1997) proved a PAC bound of $$O(\frac{1}{\varepsilon}(d+\log \frac{1}{\delta}))$$ , where $$\varepsilon$$ and $$\delta$$ are the accuracy and confidence parameters. It is still an open question whether one can obtain the same bound for intersection-closed concept classes of VC-dimension $$d$$ in general. We present a step towards a solution of this problem showing on one hand a new PAC bound of $$O(\frac{1}{\varepsilon}(d\log d + \log \frac{1}{\delta}))$$ for arbitrary intersection-closed concept classes, complementing the well-known bounds $$O(\frac{1}{\varepsilon}(\log \frac{1}{\delta}+d\log \frac{1}{\varepsilon}))$$ and $$O(\frac{d}{\varepsilon}\log \frac{1}{\delta})$$ of Blumer et al. and (1989) and Haussler, Littlestone and Warmuth (1994). Our bound is established using the closure algorithm, that generates as its hypothesis the intersection of all concepts that are consistent with the positive training examples. On the other hand, we show that many intersection-closed concept classes including e.g. maximum intersection-closed classes satisfy an additional combinatorial property that allows a proof of the optimal bound of $$O(\frac{1}{\varepsilon}(d+\log \frac{1}{\delta}))$$ . For such improved bounds the choice of the learning algorithm is crucial, as there are consistent learning algorithms that need $$\Omega(\frac{1}{\varepsilon}(d\log\frac{1}{\varepsilon} +\log\frac{1}{\delta}))$$ examples to learn some particular maximum intersection-closed concept classes.

Hierarchical learning strategy in semantic relation extraction

This paper summarizes the research on discriminative language modeling focusing on its application to automatic speech recognition (ASR). A discriminative language model (DLM) is typically a linear or log-linear model consisting of a weight vector associated with a feature vector representation of a sentence. This flexible representation can include linguistically and statistically motivated features that incorporate morphological and syntactic information. At test time, DLMs are used to rerank the output of an ASR system, represented as an N-best list or lattice. During training, both negative and positive examples are used with the aim of directly optimizing the error rate. Various machine learning methods, including the structured perceptron, large margin methods and maximum regularized conditional log-likelihood, have been used for estimating the parameters of DLMs. Typically positive examples for DLM training come from the manual transcriptions of acoustic data while the negative examples are obtained by processing the same acoustic data with an ASR system. Recent research generalizes DLM training by either using automatic transcriptions for the positive examples or simulating the negative examples.KeywordsAutomatic speech recognitionDiscriminative trainingLanguage modeling

This paper presents a novel method to retrieve images by learning the embedded visual concept from a set of given examples. Through a user's relevance feedback, the visual concept can be effectively learned to classify images which contain common visual entities. The learning process is started by providing a set of either positive or negative training examples and is then interactively adjusted according to the user's relevance feedback. In contrast to traditional methods, the proposed method utilizes a novel way to overcome the under-training problem which is frequently suffered in the learning process. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Therefore, the target concept can be learned on-line and is user-adaptable for effective retrieval of image contents. Experimental results are provided to prove the superiority of the proposed method.

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure - the nodes - are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types - vision and inertial measurements - in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.

We describe a state-of-the-art system for finding objects in cluttered images. Our system is based on deformable models that represent objects using local part templates and geometric constraints on the locations of parts. We reduce object detection to classification with latent variables. The latent variables introduce invariances that make it possible to detect objects with highly variable appearance. We use a generalization of support vector machines to incorporate latent information during training. This has led to a general framework for discriminative training of classifiers with latent variables. Discriminative training benefits from large training datasets. In practice we use an iterative algorithm that alternates between estimating latent values for positive examples and solving a large convex optimization problem. Practical optimization of this large convex problem can be done using active set techniques for adaptive subsampling of the training data.

Cross-modal hashing maps heterogeneous multimedia data into a common Hamming space to realize fast and flexible cross-modal retrieval. Supervised cross-modal hashing methods have achieved considerable progress by incorporating semantic side information. However, they heavily rely on large-scale labeled cross-modal training data which are hard to obtain, since multiple modalities are involved. They also ignore the rich information contained in the large amount of unlabeled data across different modalities, which can help to model the correlations between different modalities. To address these problems, in this paper, we propose a novel semi-supervised cross-modal hashing approach by generative adversarial network (SCH-GAN). The main contributions can be summarized as follows: 1) we propose a novel generative adversarial network for cross-modal hashing, in which the generative model tries to select margin examples of one modality from unlabeled data when given a query of another modality (e.g., giving a text query to retrieve images and vice versa). The discriminative model tries to distinguish the selected examples and true positive examples of the query. These two models play a minimax game so that the generative model can promote the hashing performance of the discriminative model and 2) we propose a reinforcement learning-based algorithm to drive the training of proposed SCH-GAN. The generative model takes the correlation score predicted by discriminative model as a reward, and tries to select the examples close to the margin to promote a discriminative model. Extensive experiments verify the effectiveness of our proposed approach, compared with nine state-of-the-art methods on three widely used datasets.

This paper studies audio-visual salient object detection. The task of salient object detection is to detect and mark the objects that are most concerned by people in the visual scene. Traditionally, visual salient object detection uses only images or video frames to detect salient objects, without modeling human multi-modal perception which includes the interaction between vision and hearing. Therefore, in order to improve the visual salient object detection, we incorporate audio modality into the traditional visual salient object detection task by applying a two-stream audio-visual deep learning network. To this end, we also build an audio-visual salient object detection dataset called AVSOD based on the existing dataset. To verify the effectiveness of audio modality in salient object detection, we compare the experimental performance of the deep learning model with and without audio modality. The experimental results demonstrate that audio modality has a good supplementary effect on the task of visual salient object detection, and also verified the effectiveness of the proposed dataset.