細部視覺分類是在影像分類問題中的一種特殊情況。這個問題之所以具有挑戰性的原因是來由於物體資訊本身存在因視角、姿勢、光照程度而造成組間的變異量小且組內變異量大的情況。為了提高分類的正確率，我們加入物體細部的位置資訊並提出一個以部位資訊為基礎的細分類流程來解決細部視覺分類的問題。
我們提出的方法包括以下幾個步驟: 首先，去除背景區域，只保留包含物體的前景區域，藉此我們可以減少因背景造成的分類干擾。第二，利用前景區域和部位資訊推測出各部位的區域分割，藉由這個區域分割的輔助，我們可以做到類似姿勢校正的效果。第三，針對各部分的區域分割分別萃取特徵，再經過特徵編碼以得到最終的照像特徵。最後我們從訓練資料中計算出類別之間的協同表示方式並一般化的最小平方誤差來進行分類。

Fine-grained visual categorization is a special case in image classification. It is a challenging task in which objects may have small between-class variation and large intra-class variation caused by viewpoints, pose and lighting condition changes. In order to improve the performance of classification, we incorporate the part information of objects and propose a part-based classification framework for fine-grained visual categorization.
The proposed classification framework consists of the following steps: First, we infer the part segmentation from foreground regions and part locations of the object. With the inferred part segmentation, we implicitly perform pose normalization on the object. Then, we extract features from the corresponding part segments and apply feature encoding to generate the final image representation. Finally, we perform image classification based on their collaborative representation with regularized least squares from the whole training data.

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