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Does learning from segmented images aid categorization?

This project aims to investigate the impact of learning from segmented images on categorization tasks. It explores the use of existing datasets or manual segmentation techniques to obtain segmented images. The modified HMAX model is used to handle feature learning using segmented images. The performance is compared between segmented and non-segmented feature learning, including cross-category comparisons and object-only images. The project also examines the effect of adding clutter to segmented objects and determines if the performance advantage is more noticeable under heavy clutter.

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Does learning from segmented images aid categorization?

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  1. Does learning from segmented images aid categorization?

  2. Motivation • Highly likely that object segmentation is crucial for visual learning during infancy • Most computer vision algorithms for categorization use unsegmented images • difficulty in getting large segmented datasets • But does learning from segmented images actually help categorization?

  3. Project A • Obtain segmented images from at least two suitable object categories • existing datasets or manually segment (LabelMe) • Modify HMAX to handle feature-learning using segmented images (easier than it sounds) • Compare performance using segmented vs. non-segmented feature-learning • cross-category comparisons • also compare to object-only images • [extra] add clutter to segmented objects • performance advantage most noticeable under heavy clutter?

  4. Project B • Literature review • Key question: how much does segmentation aid categorization and vice-versa? • Somewhat different from learning using segmented images • Starting point: algorithms that perform both tasks • Leibe, Leonardis & Schiele, ECCV 2004 • Li, Socher & Fei-Fei, CVPR 2009

  5. Resources • HMAX software • http://cbcl.mit.edu/jmutch/cns/ • Various image datasets • Weizmann horses (http://www.msri.org/people/members/eranb/) • StreetScenes (http://cbcl.mit.edu/software-datasets/index.html) • etc… • cheston@mit.edu

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