A COMPARATIVE STUDY ON MULTI-VIEW OBJECT RECOGNITION WITH APPEARANCE FEATURES AND SILHOUETTE ALIGNMENT

• Main Author: TRUONG TAN LOC
• Other Authors: Gee-Sern Hsu
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/9n234k

碩士 === 國立臺灣科技大學 === 機械工程系 === 100 === The features extracted for object recognition can be generally split into two categories, local invariant and appearance-based. The former is commonly selected for the recognition of generic objects, while the latter is a popular choice for the recognition of specific objects, for example, faces. Because most specific objects can be aligned to appearance features, the deviations from the aligned features offer some appearance characteristics good for recognition. Such an alignment can be difficult to define in generic objects. Therefore, the works on the recognition of generic objects using appearance features in the literature are significantly outnumbered by those using local invariant features. The performance of many appearance features and associated classifiers on face recognition has been widely studied and reported; however, their performance on generic object recognition is only studied in a limited scope. To extend our understanding in this regard and be able to determine the appearance features and classifiers good for generic object recognition, this paper reports a comprehensive comparison study in which different combinations of features and classifiers are evaluated on a benchmark database. To detect and segment the object of interest from a scene, which is often the first step in object recognition, we propose a scheme, called {\it Silhouette Alignment}, to align the features extracted from a test image to those in the database. Although the appearance features considered in this study are holistic, in the comparison we also include SIFT (Scale Invariant Feature Transform), one of the most popular local invariant features for object recognition, to justify the performance of the appearance-based features and associated classifiers. Experiments on the COIL-100 database show that DCT features with Naive Bayesian classifier give the best performance among others on object recognition across viewpoints. SIFT outperforms most appearance features when the image quality is degraded, i.e., blurred by noise. However, a few classifiers with appearance features outperform SIFT in both noise-free conditions and cluttered backgrounds.