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This thesis investigates the application of GIST features [13] to the problem of object detection in images. Object detection refers to locating instances of a given object category in an image. It is contrasted with object recognition, which simply decides whether an image contains an object, regardless of the object’s location in the image. In much of computer vision literature, object detection uses a “sliding window” approach to finding objects in an image. This requires moving various sizes of windows across an image and running a trained classifier on the visual features of each window. This brute force method can be time consuming. I investigate whether global, easily computed GIST features can be used to classify the size and location of objects in the image to help reduce the number of windows searched before the object is found. Using K-means clustering and Support Vector Machines to classify GIST feature vectors, I find that object size and vertical location can be classified with 73-80% accuracy. These classifications can be used to constrain the search location and window sizes explored by object detection methods.

Using GIST Features to Constrain Search in Object Detection