Existing handcrafted keypoint detectors typically focus on designing specific local structures manually while ignoring whether they have enough flexibility to explore diverse visual patterns in an image. Despite the advancement of learning-based approaches in the past few years, most of them still rely on the availability of the outputs of handcrafted detectors as a part of training. In fact, such dependence limits their ability to discover various visual information. Recently, semi-handcrafted methods based on sparse coding have emerged as a promising paradigm to alleviate the above issue. However, the visual relationships between feature points have not been considered in the encoding stage, which may weaken the discriminative capability of feature representations for keypoint recognition. To tackle this problem, we propose a novel sparsity-guided discriminative feature representation (SDFR) method that attempts to explore the intrinsic correlations of keypoint candidates, thus ensuring the validity of characterizing distinctive and diverse structural information. Specifically, we first incorporate an affinity constraint into the feature representation objective, which jointly encodes all the patches in an image while highlighting the similarities and differences between them. Meanwhile, a smoother sparsity regularization with the Frobenius norm is leveraged to further preserve the similarity relationships of patch representations. Due to the differentiable property of this sparsity, SDFR is computationally feasible and effective for representing dense patches. Finally, we treat the SDFR model as multiple optimization sub-problems and introduce an iterative solver. During comprehensive evaluations on five challenging benchmarks, the proposed method achieves favorable performances compared with the state of the art in the literature.

Most previous handcrafted keypoint methods focus on designing specific structural patterns using human-defined knowledge. These methods, however, ignore the fact that whether they have enough flexibility to harvest diverse local structures. Recently, the semi-handcrafted approaches based on sparse coding have emerged as a new trend of alleviating the above issue. And yet, the intrinsic relationships of key-points have not been explored actively, which may lead to the ambiguity of feature codes for further analysis. To tackle this problem, in this paper, we introduce a novel semi-handcrafted keypoint detector through a scheme of discriminative feature representations (SDFR). Specifically, we cast keypoint detection as an optimization problem on a visual dictionary that explicitly models the visual relationships of feature points to preserve the consistency of similar features and distance dissimilar ones. Further, we propose an iterative solver for the SDFR model. Experimental results on challenge benchmarks demonstrate that the proposed method performs favorably against state-of-the-art in literature.

We present a novel Sparsity-Aware Keypoint detector (SAKD) to localize a set of discriminative keypoints via optimization of group-sparse coding. Unlike most of current handcrafted keypoint detectors that are limited by the manually defined local structures, the proposed method has the capacity to allow flexibility for exploiting diverse structures with the combination of visual atoms from a vocabulary. Another key valuable attribute is that its group-sparsity nature concentrates on discovering sharable structural patterns across keypoints within an image jointly. This main merit facilitates to localize repeatable keypoints and resists against distractors when image undergoes various transformations. Extensive experiments on four challenging benchmark datasets demonstrate that the proposed method achieves favorable performances compared with state-of-the-art in literature.

Fine-grained categorization is a challenging task mainly due to two factors: first, objects share similar appearances between different categories; second, objects present significant pose variation within the same category. To address these challenges, we propose a method to automatically detect discriminative and pose-invariant regions, which is referred to as a polygon-based classifier (PBC). In the first stage, we generate a set of polygons that are composed of multiple parts. For each polygon, a classifier is trained based on deep features of a convolutional network. Then, a greedy algorithm is employed to select the discriminative and complementary polygon-based classifiers that deliver highest classification accuracy for fine-grained object categories. In the second stage, the confusing classes of the first stage are selected and employed to train the polygon-based classifiers. Then, a greedy algorithm is employed to select discriminative classifiers. For the test images, we use the classifiers trained in the first stage to obtain a coarse result. Then, the classifiers of the second stage are adopted to distinguish the confusing classes of the coarse result. In our experiments, the proposed approach is evaluated on three well-known fine-grained datasets. The experiments show that our approach outperforms the state-of-the-art methods.

This paper proposes a novel saliency object detection method by using the mid-level and high-level visual cues. In the mid-level objectness evaluation, we generate three complementary saliency maps, such as the multi-scale segmentation cue, the background cue and the spatial color distribution cue. The first cue is used to highlight the objects via the local region segment. The second cue uses the background priors to detect the saliency information. The third cue is to capture the spatial color distribution. For the high-level visual cue, we propose an objectness evaluation model to distinguish the object and the background. All the saliency cues are finally combined to achieve the saliency detection. The experimental results show that the proposed method outperforms the state-of-the-art saliency object detection methods.

In this paper, we exploit an algorithm for detecting the individual objects from multiple images in a weakly supervised manner. Specifically, we treat the object co-detection as a jointly dictionary learning and objects localization problem. Thus a novel low-rank and sparse representation dictionary learning algorithm is proposed. It aims to learn a compact and discriminative dictionary associated with the specific object category. Different from previous dictionary learning methods, the sparsity imposed on representation coefficients, the rank minimization of learned dictionary, data reconstruction error and the low-rank constraint of sample data are all incorporated in a unitized objective function. Then we optimize all the constraint terms via an extended version of augmented lagrange multipliers (ALM) method simultaneously. The experimental results demonstrate that the low-rank and sparse representation dictionary learning algorithm can compare favorably to other single object detection method.