Googlielmo's blog

 I am back to the blog after some months where I have been pretty busy with interesting and challenging projects and coping with the new "normal" of the COVID-19 era.  Lot of things happened and I have several stuff to share in the upcoming months. Today I am going to speak at the Big Things 2020, the Data and AI conference. This year it moved virtual. Registration is still open and for free. I hope you have a chance to attend my talk at 7:45 PM GMT+1. I am going to discuss about Adversarial Attacks to Computer Vision systems and mitigation strategies. I hope to meet you there and do also some networking in the dedicated chat area. Follow-ups for this topic will be shared in this blog in the upcoming weeks.

The Spark + AI Summit North America edition 2020 is going virtual and access to keynotes, sessions and virtual events is for free. You have to pay only to attend pre-conference and conference training, AMA sessions, VIP sessions and certification exams. The Summit will happen from June 22nd to 26th 2020. This year's keynote speakers include Francois Chollet, the creator of Keras . All details in the official website: https://databricks.com/sparkaisummit/north-america-2020

In part 1 of this series a definition of CBIR has been given. Let's now understand what's the typical flow for it. The diagram in figure 1 shows that there are two parts, one that happens offline and another which is online: Figure 1 Starting from an image storage, a preliminary trained Deep Neural Network is used to extract the features from images. Extracted features are then stored in a feature database. This happens offline any time new images are added to the storage. What happens online is the search process itself. Any time a user uploads a query image, the same Deep Neural Network used for feature extraction is used to extract the input image features. Then the distance from the query image features and the features in the database are computed. The closer the distance, the higher the relevance is. The closest features are then sorted and the corresponding images are returned as results. Basic implementation: data set preparation To make things more clear,

Content Based Image Retrieval (CBIR) systems are aimed to find similar images to an input image by querying an image data set. I am pretty sure you've used a CBIR several times before: here's the most popular one: The search in CBIRs isn't text-based as images are indexed by their visual content. Features are extracted from an image database and stored in some file system or object storage.Then, any time a new image is passed as search criterion, all those images which features are closest to the input image features are returned as results. The traditional feature extraction process relies on algorithms such as SURF or SIFT , but in this series I am going to describe my journey into CBIR using an unsupervised Deep Learning approach. Here's a preview of the first attempt, in order to search for food images (the input image in this case is an apple pie): In the next posts of this series I will walk through the details of the implementation before making thi

During these strange days of emergency we could observe, among many others from the academy and the industry, lots of initiatives by individuals or groups of Data Scientists using public available data sets to make predictions about the evolution of the COVID-19 pandemic or other healthcare related applications to help in diagnosing the symptoms of the virus whether test kits for it wouldn't be available. Every little helps, it is also wonderful to see this high level of genuine interest on this matter and I am one of those that encourage people being curious and altruistic. But, as others have already started warning lately, any personal initiative, to be effective, needs to be evaluated by subject matter experts. In this post I am going to provide a concrete example about the importance of this kind of collaboration. At the end of 2019, I got a very bad flu which then ended up in an acute bronchitis, from which I recovered very slowly. During the rest of my life, after my ch

I am so excited to be attending the Shift AI conference this April in Dublin! It's @shiftconf_co 's #ArtificialIntelligence conference where we will discuss the newest technologies in data mining, machine learning and neural networks. While Ireland is a very active hub for AI in Europe, so far there has been a lack of good conferences happening here: so, Shift AI is welcome! I hope to see you at the event. Learn more over at https://ctt.ec/2K2o6+