Relevance feedback (RF) is a technique popularly used to improve the effectiveness of traditional content-based image retrieval systems. However, users must provide relevant and/or irrelevant images as feedback for their queries, which is a tedious task. To alleviate this problem, pseudo relevance feedback (PRF) can be utilized. It not only automates the manual component of RF, but can also provide reasonably good retrieval performance. Specifically, it is assumed that a fraction of the top-ranked images in the initial search results are pseudo-positive. The Rocchio algorithm is a classic approach for the implementation of RF/PRF, which is based on the query vector modification discipline. The aim is to reproduce a new query vector by taking the weighted sum of the original query and the mean vectors of the relevant and irrelevant sets. Image feature representation is the key factor affecting the PRF performance. This study is the first to examine the retrieval performances of 63 different image feature descriptors ranging from 64 to 10426 dimensionalities in the context of PRF. Experimental results are obtained based on the NUS-WIDE dataset which contains 22156 Flickr images associated with 69 concepts. It is shown that the combination of color moments, edges, wavelet textures, and locality-constrained linear coding of the bag-of-words model provides the optimal feature representation, giving relatively good retrieval effectiveness and reasonably good retrieval efficiency for Rocchio based PRF.