Track color space-time interest points in video

Multimedia Tools and Applications - Color Space-Time Interest Points (CSTIP) are among all the interesting low-level features which can be extracted from videos; they provide an efficient...     

Fast radial symmetry for detecting points of interest

A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene. Its low-computational complexity and fast runtimes makes this method well-suited for real-time vision applications. The performance of the transform is demonstrated on a wide variety of images and compared with leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transform is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost. A real-time implementation of the transform is presented running at over 60 frames per second on a standard Pentium III PC.     

Practical simulation of virtual crowds using points of interest

We present a computational method that exploits points of interest (POIs) to generate realistic virtual pedestrians for a city model, i.e., a simulated crowd. Our method is validated using mobility traces collected longitudinally from a city-wide free and open Wi-Fi network in downtown Oulu, Finland. Analysing this data, we first construct a time-varying Origin–Destination matrix that describes how individual pedestrians in our city move at different times and places. We compare this ground-truth against a random pedestrian model to investigate how the latter underestimates or overestimates movement at various locations or times of day. By identifying these deviations, we can calibrate a weighted model that uses POIs from OpenStreetMap to adjust the simulated crowd. Our results show a significant accuracy improvement over the random model, while at the same time our work is readily applicable to simulating crowds in other cities (real and virtual) as long as POI can be defined spatially.     

Blur robust extremal region-based interest points for medical image registration

Various studies on interest point (IP) detection have concluded that maximally stable extremal region (MSER)-based IPs outperform others on repeatability, localization accuracy, robustness, efficiency and covariance to global and local image distortions. Since medical images lack sharp detail, corner IPs are not a suitable choice for them. Instead, MSERs which offer region-based IPs are useful. However, sensitivity of MSERs to image blur and scale makes them less useful practically. In this context, through this paper, following contributions are made—(1) It is proposed to study MSER-based IPs in Intensity Scale Space instead of conventional Scale Space to better understand and mitigate the problem of IP clutter. (2) By modulating the connectivity of previously proposed ER-based IPs (inspired from visual saliency approach), blur and scale sensitivity of region-based IPs is shown to reduce significantly. The newly developed IPs are called ‘blur robust extremal region (BRER)’ IPs. (3) Owing to the global nature of evaluation parameters (like repeatability) for IP detection, the problem of incorrect judgment is highlighted. As a solution to it, three new evaluation parameters called ‘Uniformity Index,’ ‘10 % core distance’ and ‘Informativeness’ are proposed. These indices capture the idea of uniform distribution of IPs over the entire image, IP clutter and the redundancy of registered IP pairs, respectively. Experiments on database of medical images of different modalities and various organs/diseases suggest that proposed BRER IPs are robust to blur and scale. Also, proposed indices of evaluation offer better judgment of quality of image registration.     

A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human–computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.     

基于兴趣点和边缘特征的图像检索方法的研究

提出了多种图像特征相结合的图像检索新方法.该方法把兴趣点和边缘作为图像中用户关注的线索,设计了一种基于兴趣点和边缘特征的图像检索新方法.它综合考虑了图像的全局形状特征、颜色及其空间分布信息,不仅克服了单一特征无法真正表征图像的缺陷,同时保证了检索算法对图像旋转、平移的识别不变性.进行检索时,可根据兴趣点的数目自适应地改变兴趣点和边缘特征的相似性权重,增加了系统的有效性.与同类方法相比较表明,该方法有效提高了图像检索的准确率.     

Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.     

Real-time gesture recognition by learning and selective control of visual interest points

For the real-time recognition of unspecified gestures by an arbitrary person, a comprehensive framework is presented that addresses two important problems in gesture recognition systems: selective attention and processing frame rate. To address the first problem, we propose the Quadruple Visual Interest Point Strategy. No assumptions are made with regard to scale or rotation of visual features, which are computed from dynamically changing regions of interest in a given image sequence. In this paper, each of the visual features is referred to as a visual interest point, to which a probability density function is assigned, and the selection is carried out. To address the second problem, we developed a selective control method to equip the recognition system with self-load monitoring and controlling functionality. Through evaluation experiments, we show that our approach provides robust recognition with respect to such factors as type of clothing, type of gesture, extent of motion trajectories, and individual differences in motion characteristics. In order to indicate the real-time performance and utility aspects of our approach, a gesture video system is developed that demonstrates full video-rate interaction with displayed image objects.     

Selective image encryption based on pixels of interest and singular value decomposition

In this paper, an efficient yet simple selective encryption technique is proposed based on Saw-Tooth space filling curve, pixels of interest, non-linear chaotic map and singular value decomposition. The core idea of the proposed scheme is to scramble the pixel positions by the means of Saw-Tooth space filling curve followed by the selection of significant pixels using pixels of interest method. Then the diffusion process is done on the significant pixels using a secret image key obtained from non-linear chaotic map and singular value decomposition. Finally, a reliable decryption process is proposed to construct original image from the encrypted image. The analysis and experimental results show that the proposed scheme can achieve various purposes of selective encryption and is computationally secure.     

Modelling spatio-temporal environmental data

A conceptual model for environmental data is presented with special emphasis on the ability to store spatio-temporal references of the data. Other aspects of the model are the ability to handle hierarchical data and semantics of the measurements. The model was tested with an implementation on an object-relational database management system. As a part of the test implementation, a forestry data set covering 75 years and 4900 hectares was loaded onto the database.     

Visual spatio-temporal function-based querying

Visual interfaces are very important for human interactions in cyberworlds. Visual spatio-temporal querying should be one of the basic tools for data mining and retrieval in cyberworlds. In this paper, we propose a novel function-based query model for arbitrary shape spatio-temporal querying. The queries are defined as geometric shapes changing over time. In our model, data are interpreted geometrically as multidimensional points with time dimension or as moving points. The queries are formulated with geometric objects and operations over them to form the query solid changing over time. The proposed query model allows us to pose arbitrary shape spatio-temporal range queries. With the uniform geometric model we integrate visual mining and querying of time-dependent data employing 3D visualization tools. It allows for creating an intuitive visual interface using 2D projections of 3D query shapes. Our approach combines visualization of spatio-temporal data with visualization of the range query formulation employing very compact function-based query model. The implemented visual query system and its visual interface are proposed and described. An example of application of the system in analysis of simulation results in molecular dynamics is considered.     

VideoZoom spatio-temporal video browser

We describe a system for browsing and interactively retrieving video over the Internet at multiple spatial and temporal resolutions. The VideoZoom system enables users to start with coarse, low-resolution views of the sequences and selectively zoom-in in space and time. VideoZoom decomposes the video sequences into a hierarchy of view elements, which are retrieved in a progressive fashion. The client browser incrementally builds the views by retrieving, caching, and assembling the view elements, as needed. By integrating browsing and retrieval into a single progressive retrieval paradigm, VideoZoom provides a new and useful system for accessing video over the Internet. VideoZoom is suitable for digital video libraries and a number of other applications in which streaming methods provide insufficient quality of video, video downloading introduces large latencies, and generating video summaries is difficult or not well integrated with video retrieval tasks     

Personalized trip recommendation for tourists based on user interests,points of interest visit durations and visit recency

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photographs. Our tour recommendation problem is modeled using a formulation of the Orienteering problem and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits and (ii) an automatic weighting between POI popularity and user interests based on the tourist’s activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F\(_1\)-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.