Feature descriptors based on local pattern have been applied successfully in image retrieval due to their simplicities. However, most of the local pattern methods only consider the relationships between the center pixel and its boundary pixels. And these methods disregard the co-occurrences between patterns in images. In this paper, we propose a novel feature extraction algorithm called intensity-based co-occurrence local ternary patterns (CLTP) using HSV color space. The brightness level at a center pixel is highly dependent on the brightness levels of its neighbors. The neighbors intensity (NI) for a given center pixel are considered in CLTP and an operator, namely NI-CLTP, is proposed. HSV color space is used in this algorithm to extract color information. NI-CLTP encodes the intensity co-occurrence of similar ternary edges among the surrounding neighbors for a given center pixel in an image and it is different from the existing local pattern methods. Furthermore, NI-CLTP is combined with Gabor transform to extract effective texture feature. Extensive experiments on diverse databases verify the effectiveness of our proposed method.