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Context and Subcategories for Sliding Window Object Recognition


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  • Product Description

The sliding window approach is the current de facto standard for building high-performance object detectors. The approach involves scanning an image with a fixed-size rectangular window and applying a classifier to the features extracted within the sub-image defined by the window. In this work, we study two important factors influencing its performance. First is the role played by context, where information outside the window is used to rescore the detections output by the local window classifier. Context helps in suppressing detections in regions that are less probable to contain an object and encourages those that are more plausible. We enumerate different sources and uses of context, and evaluate their role in aiding detection performance. Second is the role played by subcategories, where information within the window is used to split training data into smaller groups, for learning multiple classifiers to model the object appearance. The smaller groups have reduced appearance diversity and thus lead to simpler classification problems. We show that the use of subcategories not only improves detection performance, but also alleviates the need for complex representation and models.

Product Specifications
SKU :COC93080
AuthorSantosh Kumar Divvala
Number of Pages152
Publishing Year2013-10-26T00:00:00.000
Edition1 st
Book TypeTechnology: general issues
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-10-08 00:00:00
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