Optimizing visual dictionaries for effective image retrieval

Abstract

Characterizing images by high-level concepts from a learned visual dictionary is extensively used in image classification and retrieval. This paper deals with inferring discriminative visual dictionaries for effective image retrieval and examines a non-negative visual dictionary learning scheme towards this direction. More specifically, a nonnegative matrix factorization framework with 0 -sparseness constraint on the coefficient matrix for optimizing the dictionary is proposed. It is a two-step iterative process composed of sparse encoding and dictionary enhancement stages. An initial estimate of the visual dictionary is updated in each iteration with the proposed 0 -constraint gradient projection algorithm. A desirable attribute of this formulation is an adaptive sequential dictionary initialization procedure. This leads to a sharp drop down of the approximation error and a faster convergence. Finally, the proposed dictionary optimization scheme is used to derive a compact image representation for the retrieval task. A new image signature is obtained by projecting local descriptors on to the basis elements of the optimized visual dictionary and then aggregating the resulting sparse encodings in to a single feature vector. Experimental results on various benchmark datasets show that the proposed system can infer enhanced visual dictionaries and the derived image feature vector can achieve better retrieval results as compared to state-of-the-art techniques.

On integrating re-ranking and rank list fusion techniques for image retrieval

“…Based on the retrieval results of the image reranking experiments presented in Tables 2 and 3, the best four among low-level descriptors and the best three among highlevel descriptors are selected for the task of rank list fusion. Thus, PZCDM [42], SPTF [42], WDCD [50] and LTrP [53] are selected from the category of low-level descriptors and SCFVC [60], SPoC [61] and 0 -NMF [62] are chosen from the family of high-level descriptors. First of all, Table 5 summarizes the result obtained by the proposed PSO-based approach in solving the optimization problem specified in Eq.…”