Scene recognition and weakly supervised object localization with deformable part-based models

State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work.

Weakly Supervised Object Localization with Latent Category Learning

In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes.

Secondly, attribute labels are predicted by regression of the detection response magnitudes. Finally, the locations and rough shapes of pedestrian attributes can be inferred by performing clustering on a fusion of activation maps of the detection layers, where the fusion weights are estimated as the correlation strengths between each attribute and its relevant mid-level features.

Extensive experiments are performed on the two currently largest pedestrian attribute datasets, i. Results show that the proposed method has achieved competitive performance on attribute recognition, compared to other state-of-the-art methods.

Moreover, the results of attribute localization are visualized to understand the characteristics of the proposed method. Kai Yu. Biao Leng. Zhang Zhang. Dangwei Li. Kaiqi Huang. Pedestrian attribute recognition has been an emerging research topic in Multi-label image classification is a fundamental but challenging task t Pedestrian attribute recognition has attracted many attentions due to it Pedestrian attribute inference is a demanding problem in visual surveill Saquib Sarfrazet al.

Recognising semantic pedestrian attributes in surveillance images is a c Weakly supervised detection methods can infer the location of target obj Deep learning methods have achieved great success in pedestrian detectio Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The recognition of pedestrian attributes, such as gender, glasses and wearing styles, has become a hot research topic in recent years, due to its great application potentials in video surveillance systems, e.

Pedestrian attribute recognition in surveillance scene is also a challenging problem due to the low resolution of pedestrian samples cropped from far-range surveillance scenes, the large pose variations arisen from different angles of view, occlusions from environmental objects, etc.

Recently, convolutional neural network CNN has been applied for pedestrian attribute recognition. In these work, pedestrian samples cropped out from scenes are fed into an end-to-end CNN classifier outputing multiple pedestrian attribute labels. Nevertheless, to enhance the performance of attribute recognition, there are still a number of problems worthy of further studies. Firstly, some fine-scale attributes such as glass wearing are hard to recognize due to the small size of positive samples.

Secondly, some appearance features of these fine-scale attributes themselves may be easily neglected during the several alternations of convolution and max-pooling operations, so the final prediction layers of deep models cannot encode all the detailed features of fine-scale attributes for correct attribute predictions.

Thirdly, the locations of some attributes can vary significantly in the cropped pedestrian sample.Read this paper on arXiv. This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class.

This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts.

These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency.

Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.

Object localization and detection is highly challenging because of intra-class variations, background clutter, and occlusions present in real-world images.

scene recognition and weakly supervised object localization with deformable part-based models

Since those detailed annotations are expensive to acquire and also prone to unwanted biases and errors, recent work has explored the problem of weakly-supervised object discovery where instances of an object class are found in a collection of images without any box-level annotations. This paper addresses unsupervised object localization in a far more general scenario where a given image collection contain multiple dominant object classes and even noisy images without any target objects.

As illustrated in Fig. We advocate a part-based matching approach to unsupervised object discovery using bottom-up region proposals.

Histogram of Oriented Gradients (HOG) for Object Detection in Images 20110926

We go further and propose here to use these regions to form a set of candidate regions not only for objects, but also for object parts.

Objects are discovered and localized by selecting the most salient regions that contain corresponding parts. To this end, we introduce a score that measures how much a region stands out over other regions containing it. The proposed algorithm alternates between part-based region matching and foreground localization, improving both over iterations.

The main contributions of this paper can be summarized as follows: 1 A part-based region matching approach to unsupervised object discovery is introduced. Unsupervised object discovery has long been attempted in computer vision.

Sivic et al.

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Given the difficulty of fully unsupervised discovery, recent work has more focused on weakly-supervised approaches from different angles. Cosegmentation is the problem of segmenting common foreground regions out of a set of images.

It has been first introduced by Rother et al. Given the same type of input as cosegmentation, colocalization seeks to localize objects with bounding boxes instead of pixel-wise segmentations. Tang et al. These labels enable to learn more discriminative localization methods, e. In contrast, we use a large number of region proposals typically, between and as primitive elements for matching without any objectness priors. The work of Rubio et al.

For unsupervised object discovery, we combine an efficient part-based matching technique with a foreground localization scheme. In this section we first introduce the two main components of our approach, and then describe the overall algorithm for unsupervised object discovery. Then, our probabilistic model of a match confidence for m is represented by p m D.

Now, the match confidence is decomposed in a Bayesian manner:. For p m g xwe construct three-dimensional offset bins for translation and scale change, and use a Gaussian distribution centered on offset x.

The main issue is how to estimate geometry prior p x D without any information about objects and their locations. The voting is done with an initial assumption of a uniform prior over x :.

Weakly-supervised Discovery of Visual Pattern Configurations

Interestingly, this formulation can be seen as a combination of bottom-up and top-down processes: The bottom-up process aggregates individual votes into the Hough space scores Eq. Leveraging the Hough space score as a spatial prior, it provides robust match confidences for candidate matches. In particular, given multi-scale region proposals, different region matches on the same object cast votes for each other, and make all the region matches on the object obtain high confidences.Current object class recognition systems typically target 2D bounding box localization, encouraged by benchmark data sets, such as Pascal VOC.

While this seems suitable for the detection of individual objects, higher-level applications such as 3D scene understanding or 3D object tracking would benefit from more fine-grained object hypotheses incorporating 3D geometric information, such as viewpoints or the locations of individual parts. In this paper, we help narrowing the representational gap between the ideal input of a scene understanding system and object class detector output, by designing a detector particularly tailored towards 3D geometric reasoning.

In particular, we extend the successful discriminatively trained deformable part models to include both estimates of viewpoint and 3D parts that are consistent across viewpoints. We experimentally verify that adding 3D geometric information comes at minimal performance loss w.

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Figure 1. Our DPM-3D-Constraints model for object localization and viewpoint estimation can as well output part correspondences across object views which are wide appart. Deutsch Location Press.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

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Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Access provided by: anon Sign Out. Scene recognition and weakly supervised object localization with deformable part-based models Abstract: Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition.

These models have been introduced for fully supervised training of object detectors, but we demonstrate that they are also capable of more open-ended learning of latent structure for such tasks as scene recognition and weakly supervised object localization. For scene recognition, DPM's can capture recurring visual elements and salient objects; in combination with standard global image features, they obtain state-of-the-art results on the MIT category indoor scene dataset.

For weakly supervised object localization, optimization over latent DPM parameters can discover the spatial extent of objects in cluttered training images without ground-truth bounding boxes. The resulting method outperforms a recent state-of-the-art weakly supervised object localization approach on the PASCAL dataset. Published in: International Conference on Computer Vision. Article :. DOI: Need Help?We automatically discover and model "groups of objects" which are complex composites of objects with consistent spatial, scale, and view-point relationship across images.

These groups can aid detection of participating objects e. Objects in scenes interact with each other in complex ways. A key observation is that these interactions manifest themselves as predictable visual patterns in the image.

Discovering and detecting these structured patterns is an important step towards deeper scene understanding. It goes beyond using either individual objects or the scene as a whole as the semantic unit.

They are high-order composites of objects that demonstrate consistent spatial, scale, and viewpoint interactions with each other.

These groups of objects are likely to correspond to a specific layout of the scene. They can thus provide cues for the scene category and can also prime the likely locations of other objects in the scene. It is not feasible to manually generate a list of all possible groupings of objects we find in our visual world. Hence, we propose an algorithm that automatically discovers groups of arbitrary numbers of participating objects from a collection of images labeled with object categories.

Our approach builds a 4-dimensional transform space of location, scale and viewpoint, and efficiently identifies all recurring compositions of objects across images.

We then model the discovered groups of objects using the deformable parts-based model. Our experiments on a variety of datasets show that using groups of objects can significantly boost the performance of object detection and scene categorization. Step 1: Find common object patterns between every image-pair through a 4-dimensional transform space. Step 2: Clustering patterns into groups by assuming transidivity between patterns.

Allow missing participating objects: low-order groups' instantiations are merged with high-order group instantiations. Step 3: Training group detectors.

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We used the deformable part-based model. We utilize the groups to enhance two scene understanding tasks: object detection and scene recognition.

For object detection, we rescore a candidate OOI detection using a classifier that incorporates the highest detections of groups of objects in the image. For scene categorization, we represent an image using a feature vector with each dimension indicating the highest score among the detections of a certain group on the image, and then train SVM classifiers with RBF kernel for classification.

The outputs are then combined with those from other classifiers which take different feature inputs e. GIST feature, highest scores of object detections, etc. Distribution of the number of objects within our automatically discovered groups of objects using four datasets.

We can discover a diverse set of high-order groups. Evaluation on how well our automatically discovered groups correspond to the hand-generated list of 12 groups containing two objects.

Examples of our automatically discovered groups of objects from four datasets. For a full list, please refer to the paper. Choi, J. Lim, A. Torralba, and A. Exploiting hierarchical context on a large database of object categories. In CVPR, Felzenszwalb, R. Girshick, D. McAllester, and D. Object detection with discriminatively trained part-based models. Li, D.Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv.

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Some features of the site may not work correctly. DOI: Localizing objects in cluttered backgrounds is a challenging task in weakly supervised localization. Expand Abstract.

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Explore Further: Topics Discussed in This Paper Concept learning Probabilistic latent semantic analysis Convolutional neural network Unsupervised learning Baseline configuration management Information retrieval Algorithm Supervised learning Artificial neural network.

Teaching 3D Geometry to Deformable Part Models

Citations Publications citing this paper. References Publications referenced by this paper. FelzenszwalbRoss B. GirshickDavid A. Selective search for object recognition.

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scene recognition and weakly supervised object localization with deformable part-based models

With only image-level annotations, our goal is to propose a model enhancing the weakly supervised DPMs by emphasizing the importance of location and size of the initial class-specific root filter. Second, we propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a nontarget class.

Finally, we design a flexible enlarging-and-shrinking postprocessing procedure to modify the DPMs outputs, which can effectively match the approximative object aspect ratios and further improve final accuracy. It also shows competitive final localization performance with state-of-the-art weakly supervised object detection methods, particularly for the object categories that are relatively salient in the images and deformable in structures.

Article :. Date of Publication: 03 October DOI: Need Help?

scene recognition and weakly supervised object localization with deformable part-based models

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