What objects tell about actions


The CVPR 2015 paper entitled “What do 15,000 object categories tell us about classifying and localizing actions?” by Mihir Jain, Jan van Gemert and Cees Snoek is now available. This paper contributes to automatic classification and localization of human actions in video. Whereas motion is the key ingredient in modern approaches, we assess the benefits of having objects in the video representation. Rather than considering a handful of carefully selected and localized objects, we conduct an empirical study on the benefit of encoding 15,000 object categories for action using 6 datasets totaling more than 200 hours of video and covering 180 action classes. Our key contributions are i) the first in-depth study of encoding objects for actions, ii) we show that objects matter for actions, and are often semantically relevant as well. iii) We establish that actions have object preferences. Rather than using all objects, selection is advantageous for action recognition. iv) We reveal that object-action relations are generic, which allows to transferring these relationships from the one domain to the other. And, v) objects, when combined with motion, improve the state-of-the-art for both action classification and localization.

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Best paper at ACM Multimedia for VideoStory

The paper “VideoStory: A New Multimedia Embedding for Few-Example Recognition and Translation of Events” by Amirhossein Habibian, Thomas Mensink and Cees Snoek was awarded the best paper award at ACM Multimedia in Orlando. This paper proposes a new video representation for few-example event recognition and translation. Different from existing representations, which rely on either low-level features, or pre-specified attributes, we propose to learn an embedding from videos and their descriptions. In our embedding, which we call VideoStory, correlated term labels are combined if their combination improves the video classifier prediction. Our proposed algorithm prevents the combination of correlated terms which are visually dissimilar by optimizing a joint-objective balancing descriptiveness and predictability. The algorithm learns from textual descriptions of video content, which we obtain for free from the web by a simple spidering procedure. We use our VideoStory representation for few-example recognition of events on more than 65K challenging web videos from the NIST TRECVID event detection task and the Columbia Consumer Video collection. Our experiments establish that i) VideoStory outperforms an embedding without joint-objective and alternatives without any embedding, ii) The varying quality of input video descriptions from the web is compensated by harvesting more data, iii) VideoStory sets a new state-of-the-art for few-example event recognition, outperforming very recent attribute and low-level motion encodings. What is more, VideoStory translates a previously unseen video to its most likely description from visual content only.

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Conceptlets: Selective Semantics for Classifying Video Events


The IEEE Transactions on Multimedia paper: Conceptlets: Selective Semantics for Classifying Video Events by Masoud Mazloom, Efstrastios Gavves, and Cees Snoek is now available. An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are most informative per event, which we call the conceptlet. We model finding the conceptlet out of a large set of concept detectors as an importance sampling problem. Our proposed approximate algorithm finds the optimal conceptlet using a cross-entropy optimization. We study the behavior of video event classification based on conceptlets by performing four experiments on challenging internet video from the 2010 and 2012 TRECVID multimedia event detection tasks and Columbia’s consumer video dataset. Starting from a concept bank of more than thousand precomputed detectors, our experiments establish there are (sets of) individual concept detectors that are more discriminative and appear to be more descriptive for a particular event than others, event classification using an automatically obtained conceptlet is more robust than using all available concepts, and conceplets obtained with our cross-entropy algorithm are better than conceptlets from state-of-the-art feature selection algorithms. What is more, the conceptlets make sense for the events of interest, without being programmed to do so.

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Local Alignments for Fine-Grained Categorization


The IJCV paper Local Alignments for Fine-Grained Categorization by Efstratios Gavves, Basura Fernando, Cees Snoek, Arnold Smeulders, and Tinne Tuytelaars is now available. The aim of this paper is fine-grained categorization without human interaction. Different from prior work, which relies on detectors for specific object parts, we propose to localize distinctive details by roughly aligning the objects using just the overall shape. Then, one may proceed to the differential classification by examining the corresponding regions of the alignments. More specifically, the alignments are used to transfer part annotations from training images to unseen images (supervised alignment), or to blindly yet consistently segment the object in a number of regions (unsupervised alignment). We further argue that for the distinction of sub-classes, distribution-based features like color Fisher vectors are better suited for describing localized appearance of fine-grained categories than popular matching oriented intensity features, like HOG. They allow capturing the subtle local differences between subclasses, while at the same time being robust to misalignments between distinctive details. We evaluate the local alignments on the CUB-2011 and on the Stanford Dogs datasets, composed of 200 and 120, visually very hard to distinguish bird and dog species. In our experiments we study and show the benefit of the color Fisher vector parameterization, the influence of the alignment partitioning, and the significance of object segmentation on fine-grained categorization. We, furthermore, show that by using object detectors as voters to generate object confidence saliency maps, we arrive at fully unsupervised, yet highly accurate fine-grained categorization. The proposed local alignments set a new state-of-the-art on both the fine-grained birds and dogs datasets, even without any human intervention. What is more, the local alignments reveal what appearance details are most decisive per fine-grained object category.

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Attributes Make Sense on Segmented Objects


The ECCV2014 paper Attributes Make Sense on Segmented Objects by Zhenyang Li, Efstratios Gavves, Thomas Mensink and Cees Snoek is now available. In this paper we aim for object classification and segmentation by attributes. Where existing work considers attributes either for the global image or for the parts of the object, we propose, as our first novelty, to learn and extract attributes on segments containing the entire object. Object-level attributes suffer less from accidental content around the object and accidental image conditions such as partial occlusions, scale changes and viewpoint changes. As our second novelty, we propose joint learning for simultaneous object classification and segment proposal ranking, solely on the basis of attributes. This naturally brings us to our third novelty: object-level attributes for zero-shot, where we use attribute descriptions of unseen classes for localizing their instances in new images and classifying them accordingly. Results on the Caltech UCSD Birds, Leeds Butterflies, and an a-Pascal subset demonstrate that i) extracting attributes on oracle object-level brings substantial benefits ii) our joint learning model leads to accurate attribute-based classification and segmentation, approaching the oracle results and iii) object-level attributes also allow for zero-shot classification and segmentation. We conclude that attributes make sense on segmented objects.

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Locality in Generic Instance Search from One Example

The CVPR’14 paper Locality in Generic Instance Search from One Example by Ran Tao, Efstratios Gavves, Cees G. M. Snoek, and Arnold W. M. Smeulders is now available. This paper aims for generic instance search from a single example. Where the state-of-the-art relies on global image representation for the search, we proceed by including locality at all steps of the method. As the first novelty, we consider many boxes per database image as candidate targets to search locally in the picture using an efficient point-indexed representation. The same representation allows, as the second novelty, the application of very large vocabularies in the powerful Fisher vector and VLAD to search locally in the feature space. As the third novelty we propose an exponential similarity function to further emphasize locality in the feature space. Locality is advantageous in instance search as it will rest on the matching unique details. We demonstrate a substantial increase in generic instance search performance from one example on three standard datasets with buildings, logos, and scenes from 0.443 to 0.620 in mAP.


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Recommendations for Recognizing Video Events by Concept Vocabularies


The paper “Recommendations for Recognizing Video Events by Concept Vocabularies” by Amirhossein Habibian and Cees Snoek appearing in the July issue of Computer Vision and Image Understanding is now available. Representing videos using vocabularies composed of concept detectors appears promising for generic event recognition. While many have recently shown the benefits of concept vocabularies for recognition, studying the characteristics of a universal concept vocabulary suited for representing events is ignored. In this paper, we study how to create an effective vocabulary for arbitrary-event recognition in web video. We consider five research questions related to the number, the type, the specificity, the quality and the normalization of the detectors in concept vocabularies. A rigorous experimental protocol using a pool of 1346 concept detectors trained on publicly available annotations, two large arbitrary web video datasets and a common event recognition pipeline allow us to analyze the performance of various concept vocabulary definitions. From the analysis we arrive at the recommendation that for effective event recognition the concept vocabulary should (i) contain more than 200 concepts, (ii) be diverse by covering object, action, scene, people, animal and attribute concepts, (iii) include both general and specific concepts, (iv) increase the number of concepts rather than improve the quality of the individual detectors, and (v) contain detectors that are appropriately normalized. We consider the recommendations for recognizing video events by concept vocabularies the most important contribution of the paper, as they provide guidelines for future work.

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Fisher and VLAD with FLAIR


The paper “Fisher and VLAD with FLAIR” by Koen van de Sande, Cees Snoek and Arnold Smeulders will be presented as poster at the forthcoming CVPR’14 conference in Columbus, Ohio. The paper considers efficient object detection, that is automatically determining what object appears where in an image. A major computational bottleneck in many current algorithms is the evaluation of arbitrary boxes. Dense local analysis and powerful bag-of-word encodings, such as Fisher vectors and VLAD, lead to improved accuracy at the expense of increased computation time. Where a simplification in the representation is tempting, we exploit novel representations while maintaining accuracy. We start from state-of-the-art, fast selective search, but our method will apply to any initial box-partitioning. By representing the picture as sparse integral images, one per codeword, we achieve a Fast Local Area Independent Representation. FLAIR allows for very fast evaluation of any box encoding and still enables spatial pooling. In FLAIR we achieve exact VLADs difference coding, even with l2 and power-norms. Finally, by multiple codeword assignments, we achieve exact and approximate Fisher vectors with FLAIR. The results are a 18x speedup, which enables us to set a new state-of-the- art on the challenging 2010 PASCAL VOC objects and the fine-grained categorization of the CUB-2011 200 bird species. Plus, we rank number one in the official ImageNet 2013 detection challenge.

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