14585963
Effective browsing of web image search results
The rapid development of web image search engines has enabled users to search hundred million of images available on the Web. However, due to the unsatisfactory performance of current search technologies, people still need to spend much time in navigating through the large number of result pages to find images of their interest. In this paper, we analyze the user information needs for web image search results browsing and propose to employ a similarity-based organization to present the search results. A user study is carried out to compare our approach with a ranking-based list interface and a cluster-based interface. Experimental results show that similarity-based presentation can help users to explore image search results more naturally and efficiently
18003538
Automatic Synchronized Browsing of Images Across Multiple Devices
Mobile devices are undergoing considerable progress during recent years. Using these portable devices, people can easily capture and share photos even when they are on the move. Doubtlessly, browsing a large number of images on such small-form-factor devices is still hard and time-consuming, especially when they are distributed across various devices. In this paper, we propose a novel synchronized approach to facilitate image browsing across multiple devices. In this approach, similar images across multiple devices can be simultaneously presented for users to make comparatively viewing or searching. Experimental results show that the synchronized approach is beneficial to improve users' browsing experience.
11709445
Learning user interest for image browsing on small-form-factor devices
Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers.
8828427
Detecting geographic locations from web resources
The rapid pervasion of the web into users' daily lives has put much importance on capturing location-specific information on the web, due to the fact that most human activities occur locally around where a user is located. This is especially true in the increasingly popular mobile and local search environments. Thus, how to correctly and effectively detect geographic locations from web resources has become a key challenge to location-based web applications. In our previous work, we proposed to explicitly distinguish three types of locations for web resources, namely provider location, content location and serving location. Provider location is the physical location of the provider who owns the web resource; content location is the geographic location described in the web content; while serving location is the geographic scope that a web resource can reach. In this paper, we present a system that comprehensively employs a set of algorithms and different geographic sources by extracting geographic information from the web content, and mining hyperlink structures as well as user logs. As the result, only relevant geographic sources, rather than all of possible ones are used in computation of each category of web location. Finally, experimental results on large samples of web data show that our solution outperforms previous approaches.
37379077
Improving Web Browsing on Small Devices Based on Table Classification
Browsing large web pages on small screens is still very inconvenient due to their limited display sizes. A straightforward solution is eliminating the annoying horizontal scrolling requirement, i.e. present all the contents into a single narrow column, usually named one-column view. It is implemented by deleting the layout that will cause horizontal scrolling. However, after deleting the layout structure, some structural information becomes unreadable, like data tables. In this paper, we propose an approach to improve web browsing on small devices by classifying HTML tables into data tables and layout tables. For data tables, we try to preserve the original structural information in the final presentation. Therefore, the browsing experience can be improved for the one-column view. Experimental results show that our approach can achieve a satisfactory performance.
18031163
Browsing large pictures under limited display sizes
Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a set of novel approaches which are able to aid or automate common image browsing tasks on mobile devices. All of these approaches are based on an image attention model which is employed to illustrate the information structure within an image. An efficient algorithm to generate the optimal browsing path based on the information foraging theory is presented. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays
307226
Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition
Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, notmuchisknown about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groupsthe number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.
7144646
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show how to organize lateral inhibition so that groups of neurons compete to be active. Given a collection of potentially overlapping groups, the inhibitory connectivity is set by a formula that can be interpreted as arising from a simple learning rule. Our analysis demonstrates that such inhibition generally results in winner-take-all competition between the given groups, with the exception of some degenerate cases. In a broader context, the network serves as a particular illustration of the general distinction between permitted and forbidden sets, which was introduced recently. From this viewpoint, the computational function of our network is to store and retrieve memories as permitted sets of coactive neurons.
42102591
Winner Determination in Discount Auctions
Discount auctions is a market mechanism for buying heterogeneous items in a single auction. The bidders are suppliers and a bid consists of individual cost for each of the items and a non-decreasing discount function defined over the number of items. The winner determination problem faced by the buyer is to determine the winning suppliers and their corresponding winning items. We show that this problem is ${\cal NP}$-hard upon reduction from the set covering problem. The problem has an embedded network structure, which is exploited to develop heuristics and an exact branch and bound algorithm. Computational experiments were performed to evaluate the proposed algorithms.
7804224
Level-set based geometric colour snake with region support
A novel method is introduced to force a geometric-based snake be more tolerant towards weak edges and noise in images. The method integrates gradient flow forces with region constraints obtained from diffused region segmentation forces. The diffusion is obtained from the region map vector flow field. This extra region force gives the snake a global view of the boundary information within the image. We present results on both graylevel and colour images.
1308804
Texture Exemplars for Defect Detection on Random Textures
We present a new approach to detecting defects in random textures which requires only very few defect free samples for unsupervised training. Each product image is divided into overlapping patches of various sizes. Then, density mixture models are applied to reduce groupings of patches to a number of textural exemplars, referred to here as texems, characterising the means and covariances of whole sets of image patches. The texems can be viewed as implicit representations of textural primitives. A multiscale approach is used to save computational costs. Finally, we perform novelty detection by applying the lower bound of normal samples likelihoods on the multiscale defect map of an image to localise defects.