Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-the-art methods in precision with less memory and computational cost on three public datasets.

When a small pattern graph does not occur inside a larger target graph, we can ask how to find "as much of the pattern as possible" inside the target graph. In general, this is known as the maximum common subgraph problem, which is much more computationally challenging in practice than subgraph isomorphism. We introduce a restricted alternative, where we ask if all but k vertices from the pattern can be found in the target graph. This allows for the development of slightly weakened forms of certain invariants from subgraph isomorphism which are based upon degree and number of paths.  We show that when k is small, weakening the invariants still retains much of their effectiveness. We are then able to solve this problem on the standard problem instances used to benchmark subgraph isomorphism algorithms, despite these instances being too large for current maximum common subgraph algorithms to handle. Finally, by iteratively increasing k, we obtain an algorithm which is also competitive for the maximum common subgraph

Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 times speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.

TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service directly to businesses. The New York-based company is pitching studios and entertainment sites on a machine learning system that can identify products and locations as a way to generate revenue from product placements and experiential travel based on set locations. The new product is based on a year's worth of work that TheTake's development did to train a proprietary machine learning algorithm to identify images using a different technique than the industry standard, according to TheTake's chief executive Ty Cooper. Initially, the team behind TheTake would manually enter all the datasets and use an off-the-shelf computer visualization tool to identify images that fit the pre-defined parameters set by the company's staff. Companies like Universal Pictures, Comcast, Bravo, E!, Fandango, Sony Pictures and the Hallmark Channel, are testing out the AI-based service now, according to an email from Cooper.

Children learn to speak, as well as recognize objects, people, and places, long before they learn to read or write. They can learn from hearing, seeing, and interacting without being given any instructions. So why shouldn't artificial intelligence systems be able to work the same way? That's the key insight driving a research project under way at MIT that takes a novel approach to speech and image recognition: Teaching a computer to successfully associate specific elements of images with corresponding sound files in order to identify imagery (say, a lighthouse in a photographic landscape) when someone in an audio clip says the word "lighthouse." Though in the very early stages of what could be a years-long process of research and development, the implications of the MIT project, led by PhD student David Harwath and senior research scientist Jim Glass, are substantial. Along with being able to automatically surface images based on corresponding audio clips and vice versa, the research opens a path to creating language-to-language translation without needing to go through the laborious steps of training AI systems on the correlation between two languages' words.

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.

An introduction to the field of computer vision and image recognition, and how Deep Learning is fueling the fire of this hot topic. Computer Vision is an interdisciplinary field that focuses on how machines or computers can emulate the way in which humans' brains and eyes work together to visually process the world around them. Research on Computer Vision can be traced back to beginning in the 1960s. The 1970's saw the foundations of computer vision algorithms used today being made; like the shift from basic digital image processing to focusing on the understanding of the 3D structure of scenes, edge extraction and line-labelling. Over the years, computer vision has developed many applications; 3D imaging, facial recognition, autonomous driving, drone technology and medical diagnostics to name a few.

Artificial intelligence and cognitive computing seem to be in the news almost everyday – everything from how AI is now considered a legal driver of a car to Could you fall in love with an AI-enabled robot? Many of these concepts seem far-fetched and some have created concern that making computers too smart could turn computers into criminals and may backfire on the human race someday. But there have also been great strides to use AI to make precision medicine a reality and predict the outcome of trials by weighing legal evidence and moral questions of right and wrong. The reality is that today many business users, executives and even consumers are already reaping the benefits of natural language processing, machine learning, image recognition and other facets of artificial intelligence, but are blissfully unaware that these phenomena are empowered by semantics and its simple, progressive approach to maximizing data utility. So how does an AI-enabled robot or autonomous car differ from a typical software application?

The 2017 landscape for retail e-commerce is the ability to understand the customer experiences. The advances in robotics and AI are more relevant in the e-commerce space more than anywhere else. This holds not only true for marketers but also informed decisions that technology has to predict the next customer move. It's no wonder that world economic forum has dubbed 2016 as starting point for fourth industrial revolution The attention span of buyers is very small and incorrect results can lead to potential loss of customers. Machine learning (M/L) algorithms discover complex patterns or data sets that need not have to be preprogrammed.

Researchers have found that learning sign language can be beneficial for hearing adults too, giving them faster reaction times in their peripheral vision. Improved peripheral vision is useful in many sports and for driving, making you more alert to changes in your peripheral field of vision. The research also found that deaf adults have far better peripheral vision and reaction times than both hearing adults and hearing adults who use sign language. Researchers at the University of Sheffield have found that learning sign language can be beneficial for hearing adults too, giving them faster reaction times in their peripheral vision. The research, conducted at the University of Sheffield's Academic Unit of Opthalmology, found that adults learning a visual-spatial language such as British sign language (BSL) had a positive impact on their visual field response.