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Abstract:
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Object detection is a fundamental and challenging problem in computer vision.
Detecting the objects visible in an image can give us a good understanding and description of
the image. The extracted information can later be used to improve the results of other
computer vision tasks like activity recognition, content-based image retrieval, scene
recognition and more.
As technology and internet connection are becoming more accessible, billions of
people upload photos and videos every day. In order to make use of this enormous amount of
data we need to be able to extract information from these images in a quick and yet reliable
way. Convolutional neural networks (CNN) have made possible enormous progresses in
object detection and classification in recent years and have already established themself as the
state of the art approach for these problems. In this work, we try to improve object detection
performances by employing a CNN approach able to exploit object co-occurrences in natural
images. Typically, real world scenes often exhibit a coherent composition of object in terms of
co-occurrence probability. For instance, in a restaurant we typically see dishes, bottles and
glasses. We aim at using this type of knowledge as a cue for disambiguating object labels in a
detection task. |
