Bottom-Up Perceptual Organization of Images into Object Part Hypotheses

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Abstract

The demise of “segmentation-then-recognition” strategy led to a paradigm shift toward feature-based discriminative recognition with significant success. However, increased complexity in multi-class datasets reveals that local low-level features may not be sufficiently discriminative, requiring the construction and use of more complex structural features which are necessarily category independent. The paper proposes a bottom-up procedure for generating fragment features which are intended to be object part hypotheses. Suggesting that the demise of segmentation to generate a representation suitable for recognition was due to prematurely committing to a grouping option in the face of ambiguities, the proposed framework considers and tracks multiple alternate grouping options. This approach is made tractable by (i) using a medial fragment representation which allows for the simultaneous use of multiple cues, (ii) a set of transforms to effect grouping operations, (iii) a containment graph representation which avoids duplicate consideration of possibilities, and the estimation of the likelihood of a grouping sequence to retain only plausible groupings. The resulting hypotheses are evaluated intrinsically by measuring their ability to represent objects with a few fragments. They are also evaluated by comparison to algorithms which aim to generate full object segments, with results that match or exceed the state of art, thus demonstrating the suitability of the proposed mid-level representation.