Skip to main content

Deep Learning based CBIR 101 (Part 2): the basics

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, let's start a first simple implementation using a supervised Deep Learning approach for feature extraction.
The data set used for this example is the Food 101 data set. It contains images of food recipes from international cuisine classified in 101 categories (classes). Its size is about 5 GB and contains 1000 images for each class. The structure of the ZIP archive includes:
  • an image directory, which contains all the images grouped into sub-directories, one for each food class;
  • a meta directory, which contains four text files:
    • classes.txt: the list of class names;
    • labels.txt: a list of verification labels;
    • train.txt: the list of the training images;
    • test.txt: the list of the test images.
Figure 2 shows a sample of the images in this data set.


Figure 2

Unfortunately there are no separate directories for training and test images: you have to split them by yourself (a sample Python script would be provided in GitHub later on). Once the train/test split has been done, the training of the Deep Neural Network (a CNN) could start.
The next post will walk through the details of the feature extraction.

Comments

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You