12 March 2021
This blog post summarises research by one of the PEC’s researchers at Nesta, Dr Cath Sleeman, in which she analysed over 7,000 photographs of everyday objects from the Science Museum Group Collection. Below she shares a few of her findings. They illustrate how an online collection - such as a photographic dataset - allows us to study the form of objects: their shape, colour and texture. The insights gleaned can be used to enrich a museum’s catalogue and expand the ways of searching through the online collection.
We tracked the colour of objects overtime and found a substantial rise in the use of grey, and a concurrent fall in the use of brown and yellow. These trends likely reflect changes in materials, such as the move away from wood and towards plastic. A smaller trend is the use of very saturated colours which begins in the 1960s.
The video below provides a more detailed picture of how colour has evolved. The objects are ordered by their earliest date and each frame shows the mix of colours in overlapping groups of objects.
Each frame shows the 2,000 most common colours amongst a group of 250 objects. One example of an object from that group is also shown. The groups of objects overlap; 10 new objects are introduced and removed with each subsequent frame.
While objects do appear to have become a little greyer over time, we must remember that the photographs examined here are a just a sample of the objects within the collection, and the collection itself is a non-random selection of objects.
The turn towards grey can also be seen within individual objects such as telephones. The Science Museum Group Collection contains hundreds of phones, dating from the late 1800s to the present day. The video below shows that some of the earliest telephones shared the same greyscale palette that is seen today in many smartphones. The 1960s brought with it a much broader range of colours, and then the ‘greying’ began in the late 1980s, with the introduction of the brick phone.
The five most common colours in each phone are displayed behind the phone and are then added to the chart on the right-hand side.
We also examined the shape of everyday objects within the collection, and found that they appear to have become a little squarer overtime. We used machine learning to automatically group the objects (based only on their photos). Similar-looking objects were placed nearer to each other. The border around each image indicates the earliest year associated with the object.
Almost all recent objects (which have pale borders and date from the late 1940s) are found in the north-western area of the map. The objects in this area are all cuboids (or ‘box-shaped’), and range from cigarette packets to televisions, and from mobile phones to computer games.
While the cuboid or box appears to be a dominant shape for recent objects, we discovered two interesting exceptions. The first was the table telephone (found in the middle of the map) which had a much more complex shape than a cuboid, with a curly cord and handset. The second exception was modern translucent objects, which boast a variety of shapes (owing to their decorative purpose and the malleability of their materials).
We also found a small number of objects that were so ‘visually distinctive’ that they formed their own islands in the map. One such island consists almost entirely of typewriters, while another houses ancient Egyptian and Syrian weights. In the case of typewriters, its visible parts have very unique shapes - from the ribbon which is wrapped around spools, to the platten which is cylindrical.
The Science Museum Group’s open dataset of photographs allows us to learn more about their collection. Our preliminary analysis suggests that everyday objects may have become a little greyer and a little squarer (or ‘box-like’). While only time will tell, it does highlight a challenge for museums who must engage visitors with these ‘black boxes’. Collectively examining objects also allows us to identify and celebrate the most distinctive objects, from typewriters to table telephones. As computer vision methods continue to improve we will be able to extract further insights from online collections and learn more about the objects that fill our lives. For further details on the methodology please see the original article published by the Science Museum Group on Medium in October 2020.