Automating the construction of scene classifiers for content-based video retrieval

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Human-Centered Content-Based Image Retrieval

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C

Human object annotation for surveillance video forensics

A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data

Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

VITALAS at TRECVID-2008

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train the SVM classifier for the basic classifier. In fusion step, some methods were employed such as the Voting, SVM-base, HCRF and Bootstrap Average AdaBoost(BAAB)

The AXES submissions at TrecVid 2013

The AXES project participated in the interactive instance search task (INS), the semantic indexing task (SIN) the multimedia event recounting task (MER), and the multimedia event detection task (MED) for TRECVid 2013. Our interactive INS focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our INS experiments were carried out by students and researchers at Dublin City University. Our best INS runs performed on par with the top ranked INS runs in terms of P@10 and P@30, and around the median in terms of mAP. For SIN, MED and MER, we use systems based on state- of-the-art local low-level descriptors for motion, image, and sound, as well as high-level features to capture speech and text and the visual and audio stream respectively. The low-level descriptors were aggregated by means of Fisher vectors into high- dimensional video-level signatures, the high-level features are aggregated into bag-of-word histograms. Using these features we train linear classifiers, and use early and late-fusion to combine the different features. Our MED system achieved the best score of all submitted runs in the main track, as well as in the ad-hoc track. This paper describes in detail our INS, MER, and MED systems and the results and findings of our experimen