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Image Comparing and Recognition: Food Classification
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT).
2015 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Bildigenkänning och jämförelse är ett ämne som har varit i fokus under en lång tid inom datavetenskap. Många företag har försökt att skapa produkter, som utnyttjar olika lösningar för att känna igen objekt och människor. Dock har ingen lyckats skapa en lösning som kan göra detta felfritt.

Lifesum vill ha en lösning till deras kaloriräknarapplikation. Denna ska erbjuda användaren möjligheten att fotografera en maträtt, för att sedan kunna ta fram vilken maträtt som bilden illustrerar. Histogramjämförelse är ett av lösningsalternativen, dock inte den mest optimala bildjämförelsealgoritmen. Att använda en algoritm som utnyttjar nyckelpunktsdetektion är den mest optimala lösningen, om träning av algoritmen är ett alternativ.

En av idéerna för att öka precisionen är att låta användaren välja mellan de fem bästa maträtterna som algoritmen rekommenderar. På så sätt ökar man sannolikheten att maträtten som söks är en av de rekommenderade maträtterna.

Framtida arbeten inom detta ämne kan involvera forskning i hur träning utav HOG, Histogram of Oriented Gradients, algoritmen skulle fungera. Detta för att få ett bättre resultat som låter FLANN, Fast Approximate Nearest Neighbor Search Library, algoritmen arbeta snabbare.

Abstract [en]

Image recognition and comparison is a topic that has been in focus for a long time within computer science. Many companies have tried to create products that use different solutions to recognize objects and people. However, none of these companies have managed to create a solution that can do this flawlessly.

Lifesum want a solution to their calorie counting application. This will offer the user the opportunity to take a picture of a dish and then be able to retrieve which dish the image illustrates.

Histogram comparison is one solution to this problem, thought not the most optimal one. Using an algorithm that uses keypoint detection is the most optimal solution, if training of the algorithm is an option.

One of the ideas to improve the precision is to allow the user to choose between the five best dishes that the algorithm recommends. In this way one increase the probability of that the wanted dish is one of the recommended dishes.

Future work in this topic can involve researching on how training the HOG, Histogram of Oriented Gradients, algorithm would work, to get a better result that could let the FLANN, Fast Approximate Nearest Neighbor Search Library, algorithm work faster.

Place, publisher, year, edition, pages
2015. , 48 p.
Series
TRITA-ICT-EX, 2015:103
Keyword [sv]
bildanalys, bildigenkänning, matigenkänning, bild, jämförelse, matjämförelse, image, analysis, image analysis, recognition, food recognition, food, comparing, food comparison
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-177474OAI: oai:DiVA.org:kth-177474DiVA: diva2:872951
Examiners
Available from: 2015-11-25 Created: 2015-11-20 Last updated: 2017-06-15Bibliographically approved

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